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Functional programming

monad map reduce filter

pattern matching

everything is a function and function are pure (no side effect) > consequence = more robust programs > deterministe > more testable in python, only by convention, no guarantee by the language

  • immutable data structures string, tuple, collections.namedTuple lazy evaluation (generator) preserve state in functions recursion instead of loops/iteration closures functools, itertools, lambda map, filter

freezing useful when debugging legacy code replace a data structe by a frozen one and python will tell you what code tried to edit the data

__getitem__
__setitem__
    raise KeyError
freeze
    self.__frozen = True
unfreeze

disadvantages

recursion is hard (harder than loop) immutable data structure may increase run times c compliqué trouver des bons devs c compliqué

# python 3.10

from abc import ABC

class NotIterable(ABC):
    @classmethod
    def __subclasshook__(cls, C):
        return not hasattr(C, "__iter__")

def f(x):
    match x:
        case NotIterable():
            print(f"{x} is not iterable")
        case _:
            print(f"{x} is iterable")
# Python has first class functions
def create_adder(x):
    def adder(y):
        return x + y
    return adder

add_10 = create_adder(10)
add_10(3)   # => 13

# There are also anonymous functions
(lambda x: x > 2)(3)                  # => True
(lambda x, y: x ** 2 + y ** 2)(2, 1)  # => 5

# There are built-in higher order functions
list(map(add_10, [1, 2, 3]))          # => [11, 12, 13]
list(map(max, [1, 2, 3], [4, 2, 1]))  # => [4, 2, 3]

list(filter(lambda x: x > 5, [3, 4, 5, 6, 7]))  # => [6, 7]

# We can use list comprehensions for nice maps and filters
# List comprehension stores the output as a list which can itself be a nested list
[add_10(i) for i in [1, 2, 3]]         # => [11, 12, 13]
[x for x in [3, 4, 5, 6, 7] if x > 5]  # => [6, 7]

# You can construct set and dict comprehensions as well.
{x for x in 'abcddeef' if x not in 'abc'}  # => {'d', 'e', 'f'}
{x: x**2 for x in range(5)}  # => {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
def counter(maximum):
    i = 0
    while i < maximum:
        val = (yield i)
        # If value provided, change counter
        if val is not None:
            i = val
        else:
            i += 1

>>> it = counter(10)
>>> next(it)
0
>>> next(it)
1
>>> it.send(8)
8
>>> next(it)
9

In pure functional programming, the program written as a function must only work with its direct inputs, or arguments passed through the parameters. It cannot work with any other sources of information, or program state, including databases.

The key point of the functional paradigm is putting a nice wall around these stateful interactions, and distilling the functionally pure core, which is logically consistent and easy to prove to be correct.

Resources