Categories: Julia

Functions in Julia

As promised in the last article of last week, we’ll be taking a proper look at functions in Julia. And what is that exclamation mark anyway? What metaphors are appropriate for Julian functions? We’ll answer all these questions shortly

Definition

Functions are defined using the function keyword, as usual terminated with the keyword end as usual, making indentation superfluous

julia> function hi(name)
       print(name)
       end
hi (generic function with 1 method)

You can also define short functions one line

julia> hey(name)= print(name)
hey (generic function with 1 method)

And this is precisely where having LaTeX symbols in our lexicon completely distinguishes Julia. As we can name our functions so as to facilitate understanding while also keeping brevity. Done correctly, we can get our (Pluto) notebooks to read quite like math notes!

julia> Π(x::Array{Int64}) = prod(x) 
Π (generic function with 1 method)

julia> factorial(n::Int) = Π(collect(1:n))
factorial (generic function with 1 methods)

julia> factorial(2)
2

julia> factorial(3)
6

As is common, you can explicitly return a value, in which case the function stops there, or you can let the last expression be returned implicitly, as done in above functions:

julia> explicitReturn(x)=(factorial(x); return "Ha!"; print("I never show"))
explicitReturn (generic function with 1 method)

julia> explicitReturn(123)
"Ha!"

Oh, and what about those functions with an exclamation mark ? Well, that’s just a naming convention that I humbly ask you to follow when defining your own functions. Its simple, whenever your function mutates i.e. changes it’s input arguments, you should end that function’s name with !.

Typing

We can force a particular return type on a function just like we assert a DataType on a variable. If you want to read more about all that, you should read our full article on DataTypes here. But for now, just note that we use the ::operator to assert the type of a variable, and that functions are first-class objects in Julia:

julia> typedReturn(x)::Int=(factorial(x); return "Ha!"; print("I never show"))
typedReturn (generic function with 1 method)

Be careful, the above function, though defined, will never successfully execute, as it returns a string while expecting an integer!

Now, if you want a function to actually return nothing whatsoever, you’ll have to explicitly return nothing

julia> Nothing() = return nothing
Nothing

julia> Nothing() #Note the lack of output below

But what if you like gifting, and would like to return a lot of stuff? Well, you’d bundle it up in a tuple:

julia> gifter(x,y,z)=return (x,y,z)
gifter (generic function with 1 method)

julia> gifter("machine","learning","geek")
("machine", "learning", "geek")

And then you unpack the gifts at once, and give it a fancy name ‘destructuring’


julia> M,L,G=gifter("machine","learning","geek"); print("M:$M |L:$L |G:$G ")
M:machine |L:learning |G:geek 

What if you instead want to pass on a variable number of arguments? You simply add an ellipsis to the last argument, and iterate over it to get each argument( in order):

julia> function reciever(gifts...)
          [print("$gift and ") for gift ∈ gifts]
          return nothing
       end
reciever (generic function with 1 method)

julia> reciever("Hi",1,"hello",(1,2,3))
Hi and 1 and hello and (1, 2, 3) and 

return nothing end reciever (generic function with 1 method) julia> reciever(“Hi”,1,”hello”,(1,2,3)) Hi and 1 and hello and (1, 2, 3) and

Anonymous/Lambda functions

We can define functions without a name via two syntaxes. Either we can simply skip the name part in a normal definition:

julia> function(x)
           x^2
       end
#1 (generic function with 1 method)

julia> ans(10)
100

Or you can use the -> token:

julia> x->x^2
#2 (generic function with 1 method)

julia> ans(100)
10000

Canonically, we’d use them in functions like map

julia> map(x->x^2,collect(1:10))
10-element Array{Int64,1}:
   1
   4
   9
  16
  25
  36
  49
  64
  81
 100

And that’s a wrap folks. We’re almost done with this mini-series on Julia. Next up we’ll be looking at exception handling and databases.

References

Editorial Staff

Recent Posts

MapReduce Algorithm

In this tutorial, we will focus on MapReduce Algorithm, its working, example, Word Count Problem,…

1 month ago

Linear Programming using Pyomo

Learn how to use Pyomo Packare to solve linear programming problems. In recent years, with…

8 months ago

Networking and Professional Development for Machine Learning Careers in the USA

In today's rapidly evolving technological landscape, machine learning has emerged as a transformative discipline, revolutionizing…

10 months ago

Predicting Employee Churn in Python

Analyze employee churn, Why employees are leaving the company, and How to predict, who will…

1 year ago

Airflow Operators

Airflow operators are core components of any workflow defined in airflow. The operator represents a…

1 year ago

MLOps Tutorial

Machine Learning Operations (MLOps) is a multi-disciplinary field that combines machine learning and software development…

1 year ago