Learning Functional Programming

Posted on by Chris Warburton

I’ve seen a few questions about where to begin learning functional programming, so I thought I’d finally combine my individual answers into a page that I can link to in the future.

Functional Style

Functional programming is a style, which can be used in many languages, just like Object Oriented programming is a style. Some languages are created with these styles in mind (Haskell, ML, Scheme, etc. for functional; Java, C#, Smalltalk, etc. for OO), so functional programming is more natural in some languages than others.

It’s obviously easiest to do functional programming in a functional language like Haskell, with features like tail-call optimisation, currying, first-class functions, list comprehensions, etc. It’s not too bad in scripting languages like Python or Javascript, with first-class functions. It’s more difficult when functions are second-class, like in PHP or C.

If you’re trying to learn functional programming, I’d recommend to start with the “most functional” language that you’re already comfortable with. For example, if you know C and Python, go with Python since it has first class functions. As you learn about functional programming principles (no mutable state, no side-effects, etc.) try following them in your language of choice, as well as a language designed for functional programming (eg. Haskell, OCaml or Scheme).

This will do a few things:

Value-Oriented Programming

Most functional programming boils down to defining values; asking “what should this be?”. In contrast, imperative programming (eg. OO or procedural) is about performing steps; asking “what should happen next?”.

This “value-oriented programming” crops up in a lot of places, some of which are explained below. We can point out the difference using the following tongue-in-cheek example:

// An imperative mindset performs a series of actions

// Check the value of "foo"
if (foo) {
    // Set the value of "bar"
    bar = FALSE;
else {
    // Set the value of "bar"
    bar = TRUE;

// A functional mindset asks what value "bar" should have

// "bar" is the opposite of "foo"
bar = !foo;

Functional Programming Techniques

Here are a few ways to become more “value-oriented”:

Ternaries instead of if/then/else

Branches send the instruction pointer along different paths, which is a very non-functional thing to do. Instead of choosing which action to perform, try using ternary operators to choose between different values:

// Imperative
if (valid($x)) return $x;
return $default;

// Functional
return valid($x)? $x : $default;

if branches can exist without an else, which makes no sense from a value-oriented perspective. Ternaries define a value for each case:

// Imperative thinking
if ($condition) {
    $x = foo();
// $x might be undefined here!

// Functional thinking
$x = $condition? foo() : null;
// $x always has a value here

One thing to keep in mind is that too much nesting can be difficult to understand. This is the case whether we’re using branches or ternaries, but ternaries tend to look more difficult because they’re usually much more compact than branches. We can get rid of nesting by using extra variables.

Some languages have case expressions. If ternaries are the functional equivalent of if statements, then case expressions are the functional equivalent to switch statements:

// Imperative
switch ($x) {
  case 1:
  case 2:
  case 3:
    $y = "hello";
  case 4:
    $y = "world";
    $y = "foo";
# Functional
y = case x
      when 1..3 then "hello"
      when 4    then "world"
      else           "foo"

Ternary and case expressions are a limited form of the pattern-matching found in many functional languages:

case my_list of
  []        -> "Empty"
  [x]       -> "Just " ++ x
  [x, y]    -> "Both " ++ x ++ " and " ++ y
  [x, y, z] -> "Triple: " ++ x ++ ", " ++ y ++ ", " ++ z
  _         -> "A very long list!"

The cond expression found in LISP is also a more sophisticated form of ternary/case:

  ((> x 10)
   (concat x " is more than 10"))

  ((< x 0)
   (concat "0 is more than " x))

  ((equal y 100)
   "y is large")

   "None of the above")

Static Single Assignment

Mutating variables is a symptom of imperative thinking (“then do this”), whereas defining a load of constant values is a symptom of value thinking (“I’ll need this as well”). Rather than re-assigning variables to new values, try defining new variables instead:

def imperativeAverage(a, b, c):
  total  = a
  total += b
  total += c
  return total / 3

def functionalAverage(a, b, c):
  ab  = a  + b
  abc = ab + c
  return abc / 3

Instead of providing “setter” methods on objects, instantiate fresh objects:

class ImperativeObject:
    def __init__(self, x, y):

    def set_x(self, x):
        self.x = x

    def set_y(self, y):
        self.y = y

class FunctionalObject:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def with_x(self, x):
        return self.__class__(x, self.y)

    def with_y(self, y):
        return self.__class__(self.x, y)

This is much easier when functions/methods return copies, rather than mutating existing objects in-place:

def imperativeMerge(x, y):

def functionalMerge(x, y):
    return x + y

Code written in SSA style follows data flow instead of control flow

Embrace Recursion

We can use recursion instead of loops, to make everything constant:

def imperativeMap(func, lst):
    result = []
    for elem in lst:               # This keeps changing the value of elem!
        result.append(func(elem))  # This keeps changing the value of result!
    return result

def functionalMap(func, lst):
    # None of these values change; they're all constant
    return [func(lst[0])] + functionalMap(func, lst[1:]) if len(lst) > 0
        else []

Of course, many languages implement this example directly!

functionalMap = map

We can use recursion to set default values, rather than re-assigning variables:

function imperativeDefault($x, $y = null) {
  if (is_null($y)) $y = makeY($x);
  return process($x + $y);

function functionalDefault($x, $y = null) {
  return is_null($y)? functionalDefault($x, makeY($x))
                    : process($x + $y)

Of course, There’s More Than One Way To Do It. In this case, Static Single Assignment works just as well:

function functionalDefault2($x, $y_arg = null) {
  $y = is_null($y_arg)? makeY($x) : $y_arg;
  return process($x + $y);

Recursion is much easier with tail-call optimisation. Without it, stack overflows force you to limit this to a small, known number of iterations (eg. retrying a certain number of times before failing).

We can often encapsulate a loop into a generic function, then use this function to avoid stack overflows and avoid explicitly writing loops everywhere.

Separate Functionality From Structure

Functional programming is about values. Despite the name, functional programming doesn’t give functions any particularly special treatment; they’re just values like everything else (unlike, for example, in procedural or object oriented programming, where functions are not at all like other data).

By building up a large collection of small functions (possibly managed using a module system), we get a powerful toolkit for manipulating all kinds of data.

Likewise, data in functional programs is just that: data. It has no associated “behaviours”, like the kind found in object oriented programming. Thanks to this, we can use the same generic datastructures again and again (eg. List, BinaryTree, Pair, etc.), and re-use the functions we write in many projects.

This is a well-known principle in many programming paradigms: if we write 100 functions, but they involve 10 different datastructures, then on average we can only do 10 things to any particular value. Instead, if we write those 100 functions to all use the same datastructure, then we can do 100 things to any value. A great example of this is LISP, which uses the “cons cell” (a tuple of two values) to represent key/value mappings, linked-lists, tree structures, syntax trees, etc.

Code Is Inherently Interesting

In functional programming, there is less emphasis on what code is intended for than in other approaches. Instead there is much more focus on what code actually is. For example, Object Oriented code is very specialised towards a particular application:

class Person {
    private Name        name;
    private DateOfBirth dob;

    public Person(Name name, DateOfBirth dob) {
        this.name = name;
        this.dob  = dob;

    public Name getName() {
        return this.name;

    public DateOfBirth getDob() {
        return this.dob;

Functional code concentrates on the underlying structure of the solution, in this case pairs of values:

data Pair a b = P a b

first  (P x y) = x

second (P x y) = y

Applications are just a thin veneer over this structure. In this case we remove some polymorphism (the structure is accidentally too generic!) and make more descriptive aliases:

type Person = Pair Name DateOfBirth

getName = first
getDob  = second

This goes hand-in-hand with the ideas of generic datastructures and re-usable functions. In functional programming we tend to build up more and more functions to manipulate existing datastructures in new ways. Since any of these functions may be used with any other (of compatible type!), the only assumptions we can make in our code are the properties of the data structures themselves. In fact, it’s routine for casual blog posts about functional programming to contain Mathematical proofs about code based on these properties!

With all this reusable code available, there has to be a pretty compelling reason to write a new datastructure, and hence abandon all of those existing libraries. That’s why proofs are useful: we can use examples to show that something is possible with a datastructure, but to show that our desired functionality isn’t possible we need a proof.

With such an emphasis on possibilities and reasoning about properties, the code in a library/datastructure becomes the main focus of our attention; the fact that it may be well-suited to a particular application isn’t too important. As shown above, functional applications are a thin layer on top of generic libraries. If requirements change and the approach taken with the existing library isn’t suitable anymore, we can transplant this layer on to a new, more appropriate library. The libraries themselves remain useful, consistent, generic and abstract.

Compose as well as Abstract

The functions in functional programs are usually much smaller than equivalents in other styles. The reason is that small functions are much easier to re-use than large, complicated ones. In fact, we can create and combine small reusable functions using small, re-usable functions. For example, if we define function composition:

function compose(f, g) {
    return function(x) {
        return f(g(x));

Then we can avoid defining new functions like this:

function complicated(x) {
    var y = foo(x);
    var z = bar(y);
    return baz(z);

We can instead calculate new functions, like this:

var simple = compose(compose(baz, bar), foo);

By turning each part of our algorithm into a separate function, we can reuse them trivially without having to copy/paste code around. The overhead of plumbing them back together isn’t too bad if we use higher-order functions like compose. These plumbing functions are very general, so even if our language doesn’t provide many, we only have to write them once to get the benefit all over the place.

One of the examples in the section on recursion mentions the map function, which is a plumbing function for looping through lists. It can be used all over the place, and since it’s a regular function (unlike, say, a for loop), we can pass it around as a value, eg. as an argument to other plumbing functions:

# Non-functional code often conflates how to *get* values with *what to do* with
# them
result = []
for elem in collection:
    result.append(len(str(elem + 10)))

# Functional code separates the two into distinct functions
process = lambda elem: len(str(elem + 10))
result  = list(map(process, collection))

Separate Calculating What To Do From Actually Doing It

Performing irreversible actions in the middle of a calculation leads to all kinds of headaches. If you’ve ever used “dependency injection” then you’ve suffered these headaches.

By removing side-effects from our calculations, we no longer need to care which order they happen in, how many times they run, whether they’ll mess up in a test, etc.

An easy way to stop calculations from performing actions is to calculate the action without performing it. For example, if trying to calculate a user’s age requires a database access, don’t bother trying to calculate their age. Instead, calculate which DB query will get their age, and return that query. You’re then free to either run the query, or never run it and treat it just like any other value, eg. checking it during tests.

Ideas like dependency injection, mock objects, etc. disappear when we’re just dealing with actions-as-values; although these are easier to deal with when we have functions to combine them, like <*> and >>= in Haskell.

For example:

def imperative(x):
  if len(x) == 0:
    print("empty")      # Print a value
    return 1
  if len(x) > 10:
    print(str(len(x)))  # Print a value
  print("recursing")    # Print a value
  return sum(map(imperative, x))  # Recurse
val1 = imperative(...) # Run the actions and calculation
def functional(x):
  # Recurse, to get return values and printable strings
  rec = list(map(functional, x))

  recstrs =     [s      for val in rec for s in val[0]]
  recval  = sum([val[1] for val in rec])

  # Combine any recursive results with our own
  big  = [str(len(x))] if len(x) > 10 else []
  strs = big + ["recursing"] + recstrs

  # Return both the strings and the value
  return (["empty"], 1) if len(x) == 0 else (strs, recval)
(strs, val2) = functional(...)    # Run the calculation
list(map(sys.stdout.write, strs)) # Optionally, run the actions

Pass Functions Around

One good use of first-class functions is to avoid the complexities of switch/elif statements, and even simple classes, by looking up functions from a collection:

def imperativeProcess(x):
    if thing(x):
        return doThing(x)
    elif stuff(x):
        return doStuff(x)
    elif blah(x):
        return doBlah(x)
    return doDefault(x)`

def functionalProcess(x):
    funcs = {thing(x): doThing,
             stuff(x): doStuff,
             blah(x): doBlah}
    return funcs[True](x) if True in funcs else doDefault(x)

Features like Haskell-style type-classes, ML-style modules and Scala/Agda-style implicits make this easier.