Most Popular interview questions on Functional Programming

Functional programming in Python is an important topic that often comes up in interviews. Below are some popular interview questions along with their answers and code examples.

Question 1: What is functional programming?

Answer: Functional programming is a programming paradigm that treats computation as the evaluation of mathematical functions and avoids changing state or mutable data.

Question 2: What are pure functions?

Answer: A pure function is a function where the output is determined only by its input, without observable side effects.

# Pure Function Example
def square(x):
    return x * x

Question 3: Explain first-class functions in Python.

Answer: In Python, functions are first-class citizens, meaning they can be passed around as arguments to other functions, returned as values from other functions, and assigned to variables.

# First-Class Function Example
def greet(name):
    return f"Hello, {name}"

say_hello = greet
print(say_hello("John"))

Question 4: What are higher-order functions?

Answer: Higher-order functions are functions that take one or more functions as arguments or return a function as a result.

# Higher-order Function Example
def apply(func, value):
    return func(value)

print(apply(square, 5))

Question 5: What is a lambda function?

Answer: A lambda function is a small anonymous function defined with the lambda keyword.

# Lambda Function Example
double = lambda x: x * 2
print(double(4))

Question 6: How to use map() in Python?

Answer: map() applies a function to all items in an input list.

# map() Example
result = map(square, [1, 2, 3, 4])
print(list(result))

Question 7: Explain filter() in Python.

Answer: filter() filters the elements of a list based on a function, returning a new list with elements that evaluate to True.

# filter() Example
result = filter(lambda x: x % 2 == 0, [1, 2, 3, 4])
print(list(result))

Question 8: What is reduce() in Python?

Answer: reduce() applies a rolling computation to sequential pairs of values in a list, reducing the list to a single value.

from functools import reduce

# reduce() Example
result = reduce(lambda x, y: x*y, [1, 2, 3, 4])
print(result)

Question 9: What are closures in Python?

Answer: A closure is a function object that has access to variables in its enclosing lexical scope.

# Closure Example
def outer(x):
    def inner(y):
        return x + y
    return inner

closure = outer(10)
print(closure(5))

Question 10: What is currying in Python?

Answer: Currying is the technique of breaking down a function that takes multiple arguments into a series of functions that each take a single argument.

# Currying Example
def multiply(x):
    def wrapper(y):
        return x * y
    return wrapper

double = multiply(2)
print(double(4))

Question 11: Explain recursion in functional programming.

Answer: Recursion is a technique where a function calls itself.

# Recursion Example
def factorial(n):
    if n == 1:
        return 1
    return n * factorial(n-1)

Question 12: What is immutability?

Answer: Immutability refers to the idea that data should not be changed once created.

# Using tuple for immutability
x = (1, 2, 3)

Question 13: How do you use the zip function?

Answer: zip is used to combine multiple iterables element-wise.

# zip() Example
result = zip([1, 2, 3], ['a', 'b', 'c'])
print(list(result))

Question 14: What are decorators in Python?

Answer: A decorator is a design pattern that allows you to add new functionality to an existing object without modifying its structure. In Python, decorators dynamically alter the functionality of a function or class.

# Decorator Example
def my_decorator(func):
    def wrapper():
        print("Something is happening before the function is called.")
        func()
        print("Something is happening after the function is called.")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()

Question 15: What are Python Generators?

Answer: Generators are iterators that generate values on-the-fly and do not store them in memory.

# Generator Example
def count_up_to(n):
    count = 1
    while count <= n:
        yield count
        count += 1

Question 16: How to create an infinite sequence in Python?

Answer: You can create an infinite sequence using generators.

# Infinite sequence generator
def infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1

Question 17: What is the role of __iter__() and __next__() in Python?

Answer: __iter__() returns the iterator object and __next__() returns the next value from the iterator.

# __iter__() and __next__() Example
class MyRange:
    def __init__(self, n):
        self.n = n
        self.i = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.i < self.n:
            value = self.i
            self.i += 1
            return value
        else:
            raise StopIteration

Question 18: Explain the concept of “Lazy Evaluation.”

Answer: Lazy evaluation means that the evaluation of an expression is delayed until its value is actually needed. Generators in Python are an example of lazy evaluation.

# Lazy evaluation with generator
lazy_eval = (x*x for x in range(1, 11))

Question 19: How is functools.partial used in Python?

Answer: functools.partial allows you to fix a certain number of arguments of a function and generate a new function.

from functools import partial

# functools.partial Example
def multiply(x, y):
    return x * y

double = partial(multiply, 2)
print(double(4))

Question 20: How is functional programming different from object-oriented programming?

Answer: In functional programming, the focus is on what you are computing, whereas in object-oriented programming, the focus is on the objects that you are manipulating.