The Essence of Programming - Functional Approach

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This blogpost is a general overview of a rather underappreciated programming methodology called functional programming. Throughout the blogpost, I will occasionally use the purely functional programming language Haskell as well as an imperative-style programming language Python. I will be assuming the knowledge of basic programming concepts such as variable assignment, arithmetic operations, conditionals, functions, loops, and recursion.

It is important to note that the blogpost is just an introduction to the paradigms in functional programming and does not cover any of them in great detail.

What is Functional Programming?

As mentioned above, functional programming is just an approach to programming. Particularly, it refers to programming using functions, hence the name functional programming. To better understand what it means for a programming language to be functional, let's make a short side-by-side comparison of functional and, wildly popular, imperative style of programming languages and then discuss the differences in more detail.

Imperative languageFunctional language
Classes or structures are the first-class citizensFunctions are the first-class citizens
State changes are importantState changes are limited or non-existent
Primary control flow: loops and conditionalsPrimary control flow: function calls and recursion

Classes VS Functions

The first comparison shows that, generally speaking, in the imperative languages (i.e., Python, C, Java, etc.), variables (instances of classes or structures) dominate over all other objects. Thus, imperative paradigm makes a clear distinction between variables and functions. On the other hand, in functional programming languages, functions are the first-class citizens making virtually everything else rank below them.

Imperative programming languages treat variables as data, while functions are generally used just to manipulate variables or generate data. When programming in a functional language, we say that functions are very similar to variables. In fact, we say that they are no different than variables as they not only manipulate the data, but also represent the data themselves. Thus, in the functional world, we say that the piece of code like a function is also data.

Not totally related to the topic, but there is a term describing a language if a program written in it can be manipulated as data. This quality is referred to as homoiconicity and such languages are called homoiconic. One of such languages is Lisp. And, based on our discussion points, it is not very surprising that Lisp is a functional language.

I will give you a concise proof of why functions are data. Remember the table representations of functions we learned at some point in the elementary school? That's the proof! Any function can be represented as a table of values. For instance, consider a function \( f(x) = 2x \). The following will be a table representation of the function.

\( x \)\( f(x) \)

Looks more like data? That's because it is the data! We have effectively generated a 2-column table where each of the cells has a certain value. And yes, this is very similar to SQL tables and polaris data frames.

Natural Outcomes

Because functions are so important, there are natural outcomes which are shared among most of functional languages.

Let's write a Haskell function to find the factorial of a positive integer.

-- | A function to find the factorial of a positive integer
factorial x = product [1..x]  -- easy as that

The function builds a list of integers from 1 up to x and then calculates the product of these elements. This way we effectively get a product \( 1 \times 2 \times 3 .. \times \ x \) which is the same as \( x! \). Since we now have a function, we can call it with the actual parameters!

print (factorial 1)  -- prints out 1
print (factorial 6)  -- prints out 720
print (factorial 9)  -- prints out 362880

As shown above, something that in imperative languages would require importing modules, looping, etc. is a single line in Haskell. This is one of the outcomes of functions being first-class citizens. Most functional languages have a rich pool of built-in/predefined functions that help manipulate data. In the example above, we also see a very interesting notation. Namely, [1..x] which builds up a list of integers from 1 up to \( x \) (\( x \) must also be an integer such that \( x \geq 1 \)). Thus, another outcome is that data structures and collections can be created very easily, usually just in a single line of code, leaving more time for the programmer to deal with functions and the logic. These are some of the reasons why functional languages are so concise.

Math, Sets, and Haskell

Notice that in the factorial function above, I excluded the case when the function is called with \( 0 \) (\( 0! = 1 \)). It was done on purpose so that now we are able to add some other notation and explain the whole function in detail. Below is a better and more complete version of the function.

-- | A function to find the factorial of a number
factorial :: Integer -> Integer
factorial 0 = 1
factorial x = product [1..x]

:: - prompts that it is a function declaration

Integer - type that can hold any number no matter how big, up to the limit of the machine's memory.

-> - tells either what is the type of the next formal parameter or what is the type of the output

Looks similar to something you have seen before? If you have taken any undergraduate math class, there is a chance that you've encountered the following notation:

\[ f : A \rightarrow B : x \mapsto y \]

The notation above describes a simple function that takes an input from set \( A \) and maps it to the output in the set \( B \).

Here is the complete definition of the factorial function that we saw above:

\[ f : \mathbb{Z}^+ \cup \{ 0 \} \rightarrow \mathbb{Z}^+ : x \mapsto x! \]

Haskell defines in the similar fashion.

factorial :: Integer -> Integer says that factorial is a function that takes an element from the set of integers and maps it to some other element in the set of integers. As opposed to math, however, Haskell does not use \( \mapsto \) notation and instead has the statements below the definition.

factorial 0 = 1
factorial x = product [1..x]

The code above is equivalent to saying that if the element is 0, map it to 1 and in all other cases, map it to the product from 1 up to the element.

State Changes and Functional Programming

Functional languages have a limited notion of state and typically, avoid the shared mutable state at any cost. Purely (see Purely Functional Languages) functional languages like Haskell, do not have any state at all. Since there are no changes in state, there are no mutable variables. Instead, functional languages offer functions and immutable variables.

To make it clear, let's look at two examples below. One is from Python and the other is from Haskell.

Python example

# Define a variable 'my_number' and assign it to 3
my_number = 3

# Increment the variable 'my_number' by 1 and reassign it to the result
my_number += 1

print(my_number)  # Prints out 4

Let's repeat the same steps in Haskell.

myNumber = 3    -- Define a variable 'myNumber' and assign it to 3
myNumber += 1   -- Haskell gags here (infinite loop)

print myNumber  -- This statement is not reachable

Looking at the code above, you might have already noticed that Haskell does not allow for changing the state of the program. Now, you might be wondering how could one increment variables.

Here is a short answer:

myNumber = 3                  -- Define a variable 'myNumber' and assign it to 3
myOtherNumber = myNumber + 1  -- Define a variable 'myOtherNumber' and assign it to 'myNumber'
myNumber = myOtherNumber      -- Redefine 'myNumber' and set it to 'myOtherNumber'
print myNumber                -- Prints out 4

Longer and better answer

You do not really need such increments or decrements in functional programming languages. You can easily overcome this hindrance through functions and recursion. Therefore, instead of mutating objects, we use recursion to gradually get to the target.

Here is the example of how one could translate a well-known accumulator pattern from Python to Haskell.

Here is a classic Python accumulator pattern:

# An accumulator pattern approach for finding
# the sum of the first 100 positive integers.

total = 0
for integer in range(1, 101):
    total += integer

print(total)  # Prints out 5050

Here is what it looks like in Haskell:

accumulator 1 = 1                        -- The base case for the recursion
accumulator x = x + accumulator (x - 1)  -- The recursive case

main = print (accumulator 100)  -- Prints out 5050

In the code excerpt above, we did not use any loops. In fact, we could not use any loops because functional languages do not support loops. Instead, we defined a function, used the recursion and calculated the sum of the values through function calls.

Side Note

In this particular case, we do not even need to implement the recursive accumulator pattern. All we need to do is use the already predefined sum function and so-called texas range list notation that we have already seen ([1..x]):

print (sum [1..100])  -- Prints out 5050

Control Flow

As we have already seen, there are no for loops or while loops in functional programming languages and there are good reasons why. Let's list a few of them and continue our discussion by elaborating on those reasons.

Functional Languages Are Declarative

For those who are new to the idea of declarative languages, let's first discuss what it means for a language to be declarative. Here is a simple definition:

Declarative programming is a method of programming that abstracts away the control flow for logic required for performing an action, and instead involves stating the task or desired outcome.

The examples of declarative languages are SQL, Haskell, Prolog etc.

Example 1: Consider the SQL querying language. In SQL, one doesn't describe what how to get the data. One just tells SQL what data is needed, and SQL engine figures out the best way to get it.

Example 2: A better example might be comparing two implementations of a simple function. Let's implement them in both Python and Haskell.

The function takes a list of integers and returns the sum of odd integers in it.

def odd_sum(list_of_integers):
    """Returns the sum of all odd integers in the list."""

    total = 0
    for integer in list_of_integers:
        if integer % 2 == 1:
            total += integer
    return total

print(odd_sum([1, 2, 3, 4, 5]))  # Prints out 9

Let's do a shallow analysis of the odd_sum function. As seen above, it starts by declaring a variable total which is initially set to 0. Then, we iterate over the list and through each iteration, we check if the integer is odd and if it is, we add it to total. In the end, we return the total variable.

Now, that we have analyzed the function a bit, notice that in the for loop, through each iteration, we are giving Python directions when to add the integer to total (only if it is odd). Thus, we tell Python what to do step-by-step. This is an important characteristic that distinguishes non-declarative languages from declarative ones.

Let's now look at the Haskell example.

oddSum x = sum (filter odd x)

main = print (oddSum [1,2,3,4,5])  -- Prints out 9

Notice what we did here. First we defined a function oddSum which takes a list. Then we used the function filter (happened to be predefined) in conjunction with another predefined function odd (returns true if the value is odd an false otherwise) to get the list of odd integers. Finally, we summed up all the odd integers and got the result.

See the difference? In Python, we used a for loop and through each iteration, we told Python whether to add the integer it to total or not. In Haskell, however, we gave a whole list to the function and told it to just remove all of the even integers from the list and then to sum up the rest (if you eliminate all the even integers, you are obviously left with all the odd integers). In other words, in the Haskell example, we do not care how the functions sum and filter work internally, we only care about the fact that they do their job - sum up the odd numbers in the list and return the value.

Functional Programming And Lambda Calculus

Lambda calculus (also written as \( \lambda \)-calculus) is a branch of mathematics which was developed by Alonzo Church in the 1930s. It is a formal system for expressing computation and an alternative to what's called Turing machine which was introduced by Alan Turing. Turing machines involve loops and other non-declarative approaches (Turing machines are the inspiration for programming languages like Java, Python, etc). A few years later, Church and Turing collaboratively wrote a paper which is now know as the computability thesis and proved that all the computation that was done using Turing machines could effectively be done in lambda calculus as well. Hence, simply put, lambda calculus has the power equivalent to that of Turing machines. Not too long after, people decided to base programming languages on the ideas in lambda calculus (it was just as powerful as Turing machines so why not?!). This led to shared characteristics among functional languages such as lack of loops. Virtually all functional programming languages have no loops because lambda calculus has no loops. One could certainly add loops, but they would have been redundant. Instead, functional languages use a mathematical idea of recursion. This is the part of the reason why loops are not that appreciated in the functional world.

Getting Rid of Loops

Despite the fact that sometimes they are very useful, loops must not be a part of a functional programming language. There are several reasons for this.

  1. Loops are imperative, prompting the language what to do.
  2. Loops usually involve mutating values which is, once again, against functional virtues.
  3. Even if we did not use it imperatively and not mutate values, it would create unnecessary redundance in a language with the emphasis on recursion (which is just as powerful as a regular loop!)

Purely Functional Languages

You might have seen word pure in the beginning of the blogpost where I mentioned that Haskell is purely functional programming language. However, I never defined what it means for a functional language to be pure. So let's do it now!

Those who read the Math, Sets, and Haskell, remember the math notation for functions? I will use them to take the mystery out of this concept of being pure!

Suppose we have a function \( f : \mathbb{Z} \rightarrow \mathbb{Z} \). Then by just looking at the function, we see that it takes an input from a set of integers and its output is also in the set of integers. In other words, function \( f \) cannot take inputs like -1.9, 0.2, 12.7 etc. as well as it cannot give an output like 12.6, 71.9, -9.1 etc. Its input(s) and output(s) could only be integers.

Now, let's actually make this dull function \( f \) do something. Consider the function \( f : \mathbb{Z} \rightarrow \mathbb{Z} : x \mapsto 2x \). Thus, we have a function which does a fairly straightforward thing: takes an integer and maps it to twice its value (which will also be an integer). Let's now look at the Haskell implementation of this function

-- | A function that takes an input and outputs twice its value
f :: Integer -> Integer
f x = 2 * x

The function above says that the input (corresponds to the Integer before the arrow) is always an integer and the output (corresponds to the integer after the arrow) is also an integer. Hence, we always know what type is the input and what type is the output. In fact, we also know that the if we call a function with say 5, we will always get the same result. Namely, f 5 = 10. Hence, we got that input(s) and output(s) are always integers and the function called with same actual parameters always return the same value! This is what makes Haskell a purely functional language. At any given point in time, we always know what is the type of input and what is type of output. Besides, we know that the function called with the same actual parameter(s), always returns the same value. Such functions virtually never produce side effects since we already know what to expect for a given input. Such functions are called pure!

To further demystify this idea, let's look at the following piece of code:

An example of a function that is pretending to be pure.

import random

def numgen(val: int) -> int:
    """Generates a number."""

    return val + random.randint(1, val) % 3

def main() -> None:
    """Test the number generation."""

    print(f"Returns {numgen(7)}")  # Prints out 8
    print(f"Returns {numgen(7)}")  # Prints out 8
    print(f"Returns {numgen(7)}")  # Prints out 7

if __name__ == "__main__":

Here, we defined a function that takes an integer value as an input and it seems like the output is also an integer. We now might be lured into thinking that function numgen gives the same output for the same input, but that is clearly not the case here. Let's take a closer look at what the function does. It takes an integer value and returns the value plus some random number which is 0, 1 or 2. When we first called the function with the actual parameter 7, we got 8 as an output. The second time, we got 8 again. The third time however, we got 7. Hence, for the third time, the output was not the same. Therefore, the function is not pure.

You now might wondering why I could not do the same trick in Haskell. In fact, I certainly can. However, in Haskell, such function would not have a type Int. It would have a type IO Int. IO is usually associated with file input / output and it is reasonable that it is associated with functions that are not always "truthful" as File I/O could in fact be one of the nastiest experiences for a programmer. So many things can go wrong! (e.g., writing to a file which was deleted, reading from a file on a USB which was ejected, writing a file that was moved to some other directory etc). Thus, when we deal with uncertainty (which usually comes with side effects), Haskell warns us by using the IO notation:

Example of a function that if called with the same argument,
does not always return the same result.

import System.Random (randomRIO)

notAPureFunction :: Int -> IO Int
notAPureFunction value = do
    randomValue <- randomRIO (0,2)
    return (value + randomValue)

main = do
    x <- notAPureFunction 7
    print x                  -- Prints out 9
    x <- notAPureFunction 7
    print x                  -- Prints out 7
    x <- notAPureFunction 7
    print x                  -- Prints out 9

Take a look at the Haskell code above. You can disregard all the notational fluff. Just look at the return type of the function notAPureFunction. It is IO Int! In other words, Haskell informs us that the function might have side effects.

Finally, we can have a rough definition of a pure functional language:

A functional language is pure if and only if the user is informed about all side effects or there are no side effects at all.

In fact, Haskell did not even allow random values back in 1990s when its development was first launched. Furthermore, there was no notion of File IO either and writing to files was done using shell redirection commands (i.e., runhaskell Program.hs > out.txt). Because of this, Haskell was considered useless for all practical purposes. Eventually, engineers and the Haskell committee decided to change the direction of Haskell. In lieu of getting rid of all the side effects, they decided to control the side effects and created a more "regulated" programming environment.


Functional programming languages are different from imperative ones. Most of them are based on ideas in lambda calculus. Functional languages are the proper subset of declarative languages. There are no loops and recursion is used instead. Changes in state are non-existent and therefore, all the variables are immutable. Functional languages usually have a lot of predefined functions to make it easy for a programmer to solve problems. Most of functional languages are also very concise, minimizing the time spent on coding and leaving more time for the logic. Pure functional languages are the proper subset of functional languages. Purely functional languages go a long way to inform the user about potential side effects.

How to Get Started with Functional Programming?

There are lots of functional languages. One will obviously have to decide which one to learn first. My recommendation would be learning Haskell. It is a purely functional programming language which has most of (if not all) functional ideas in it. Besides, SPJ dedicates most of his time on extending the language and adding new features to it. So if there is something new and interesting in the functional programming world, Haskell will likely adopt it.

After learning one functional language, it is not all that difficult to transition to the other. Being familiar with one functional language automatically makes one somewhat familiar with others. Hence, a good understanding of Haskell will make it easy to learn languages such as Rust, Scheme, etc.

To get started, visit the Haskell Documentation page which is full of various educational resources.