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Functional Programming

Main styles of programming / paradigms:

  • Imperative Programming (IP) = computation in terms of statements that change a program state from initial to result

  • Object-oriented Programming (OOP) = computation based on manipulation on collections of objects. Objects have internal state and support methods that query or modify this internal state in some way.

  • Declarative Programming (DP) = expressing the logic of a computation without describing its control flow

    • Functional Programming (FP) = computation as the evaluation of mathematical functions and avoids state and mutable data apply transformation (and compositions)

In other words:

  • Imperative Programming

    • a sequence of steps/instructions that happen in order (some algorithm) to calculate needed result

  • Functional Programming

    • a set of functions that transform data into needed result

Per language:

  • Imperative programming

    • C, C++

    • Pascal

    • Basic, Sh

    • Python

  • OOP

    • Java

    • Smalltalk

    • C++

    • Python

  • Declarative programming

    • Functional programming

      • Haskell

      • Scheme

      • OCaml

      • Python

    • Logic programming (Prolog, Clojure core.logic)

Python is flexible allowing most programming styles

What is mostly used in Imperative Programming paradigm:

  • while statements

  • for loops

  • functions

  • assignments and changing variables

How Functional Programming is different:

  • loops -> recursion, map, filter, reduce, comprehensions

  • changing variables -> no changing variables once set, instead - immutable types: tuple, frozenset

  • functions -> return value is only dependent on this function's input

def imp_function():
    res = 0
    do_this()
    do_that()
    do_and_that()
    print(res)
def fp_function(data):
    return do_and_that(do_that(do_this(data)))
def imp_function():
    res = 0
    a = data.split()
    for x in a:
        res += int(x)
    print(res)
def fp_function(data):
    return sum(map(int, data.split()))

Abstract and not abstract examples:

Task:

Evaluate expression ignoring some mistakes:

"1+2+22+++100++48"

We need to calculate in this manner: 1+2+22+100+48

Imperative approach

🪄 Code:

expr = "1+2+22+++100++48"
res = 0
 
for t in expr.split("+"):
    if t != "":
        res += int(t)
print( res )

📟 Output:

173

Functional approach

🪄 Code:

sum(map(int, filter(bool, expr.split("+"))))

📟 Output:

173

Don't freak out - we'll cover all of this.

One more example that shows how different reading of some code weritten via imperative programming style or functional is.

Let's try to guess quickly what the following two functions do.

Imperative:

def mystery_imp(x):
    r = 0
    for c in x:
        cl = c.lower()
        if cl == "a" or cl == "b" or cl == "c":
            r += 1
    return r

Functional:

def mystery_fp(str_):
    return len(list(filter(lambda x: x.lower() in "abc", str_)))

Yes, both of them are case-insensitively counting 'a', 'b' and 'c' characters in a string:

🪄 Code:

str_ = "A beautiful example of some test string"
print(mystery_imp(str_))
print(mystery_fp(str_))

📟 Output:

4
4

Functional is a bit easier to understand...

Map

map(function, iterable1, iterable2, ..., iterableN)

Return an iterator that computes the function using arguments from each of the iterables. Stops when the shortest iterable is exhausted.

On Python 2 - map returns list.

For Python 3 sometimes it's better to use list comprehensions

🪄 Code:

print(list(map(str, [1, 2, 3])))
print(list(map(pow, [2, 5], [3, 4])))
print(list(map(lambda x,y: x**2 + 2*x*y + y**2, range(10)[::-1], [10]*10)))

📟 Output:

['1', '2', '3']
[8, 625]
[361, 324, 289, 256, 225, 196, 169, 144, 121, 100]

🪄 Code:

print([str(x) for x in [1, 2, 3]])
print([pow(x[0], x[1]) for x in zip([2, 5], [3, 4])])
print([pow(x, y) for x,y in zip([2, 5], [3, 4])])

📟 Output:

['1', '2', '3']
[8, 625]
[8, 625]

🪄 Code:

print(list(map(sum, [[1, 2, 3] , [4, 5, 8]])))
print(list(map(lambda x: x+1, [1, 2, 3])))

📟 Output:

[6, 17]
[2, 3, 4]

Filter

filter(function, iterable)

Construct an iterator from those elements of iterable for which function returns True

🪄 Code:

print(list(filter(bool, [1, 2, 0])))
print(list(filter(lambda x: x > 0, [-2, -1, 0, 1, 2])))
print(list(filter(lambda x: x%2, [1, 2, 3, 4, 5, 6, 7, 8])))
print(list(filter(lambda a: int(a) % 2 if isinstance(a, int) else True, [1,2,3,4,5,0, "a", [], 0.0, False, None])))
print(list(filter(lambda a: int(a) % 2 if isinstance(a, int) else False, [1,2,3,4,5,0, "a", [], 0.0, False, None])))

📟 Output:

[1, 2]
[1, 2]
[1, 3, 5, 7]
[1, 3, 5, 'a', [], 0.0, None]
[1, 3, 5]

Zip

zip(*iterables)

Make an iterator that aggregates elements from each of the iterables.

Simply speaking zip() creates pairs from elements of provided arguments

🪄 Code:

print(list( zip([1, 3], [2, 4] )))
print(list( zip([1, 2, 3, 4], [1, 2, 0]) ))
# Unzipping
zipped = zip([1, 2, 3, 4], [1, 2, 0])
print(list( zip(*zipped) )) # Note that we'll loose element from longer list

📟 Output:

[(1, 2), (3, 4)]
[(1, 1), (2, 2), (3, 0)]
[(1, 2, 3), (1, 2, 0)]

## Other perls of FP

  • Avoid state –

  • Immutable data –

  • First-class functions –

  • Higher-order functions –

  • Pure functions –

  • Recursion, tail recursion –

  • Iterators, sequences, lazy evaluation, pattern matching, monads...

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