10 Hardest Python Interview Questions

Python's popularity in software development, data science, and web applications makes it a staple in technical interviews. However, beyond basic syntax and data structures, interviewers often probe deeper into Python's internals, concurrency, and advanced features to gauge a candidate's expertise.

This article explores 10 of the hardest Python interview questions, complete with explanations, sample code, and insights into why they trip up even seasoned developers. These questions test conceptual understanding, problem-solving, and the ability to navigate Python's quirks. Whether you're preparing for a FAANG interview or a senior role, mastering these will set you apart.

1. Explain Metaclasses and How They Work in Python

Metaclasses are often called "classes of classes" because they define the behavior of classes themselves. In Python, everything is an object, including classes, which are instances of metaclasses. The default metaclass is type, but you can create custom ones by inheriting from type and overriding methods like __new__ or __init__.

class Meta(type):
    def __new__(cls, name, bases, dct):
        dct['added_attr'] = 42
        return super().__new__(cls, name, bases, dct)

class MyClass(metaclass=Meta):
    pass

print(MyClass.added_attr)  # Output: 42

This pattern is used in frameworks like Django, where metaclasses enforce model field definitions and validations.

2. What Are Descriptors and How Do They Implement Properties?

Descriptors are objects that define one or more of the methods __get__, __set__, or __delete__. They allow custom attribute access and are the mechanism behind @property, @classmethod, and @staticmethod.

class Descriptor:
    def __get__(self, instance, owner):
        return instance._value if instance else "Class access"

    def __set__(self, instance, value):
        instance._value = value

class MyClass:
    attr = Descriptor()

obj = MyClass()
obj.attr = 10
print(obj.attr)        # 10
print(MyClass.attr)   # "Class access"

3. Generators vs Iterators

Iterators implement __iter__ and __next__ manually. Generators are a special type of iterator created using yield, which automatically handles state.

def fibonacci(n):
    a, b = 0, 1
    for _ in range(n):
        yield a
        a, b = b, a + b

for num in fibonacci(5):
    print(num)

4. Python Garbage Collection and Reference Cycles

Python primarily uses reference counting, supplemented by a generational garbage collector to detect reference cycles.

import gc

class Node:
    def __init__(self):
        self.ref = None

a = Node()
b = Node()
a.ref = b
b.ref = a

del a, b
gc.collect()

5. The Global Interpreter Lock (GIL)

The GIL ensures that only one thread executes Python bytecode at a time in CPython. This simplifies memory management but limits CPU-bound multithreading.

6. Closures and Variable Scope

Closures occur when an inner function captures variables from its enclosing scope, even after the outer function has returned.

def outer(x):
    def inner(y):
        return x + y
    return inner

closure = outer(10)
print(closure(5))  # 15

7. Decorators with Arguments

Decorators that accept arguments are actually decorator factories—functions that return decorators.

def repeat(times):
    def decorator(func):
        def wrapper(*args, **kwargs):
            for _ in range(times):
                func(*args, **kwargs)
        return wrapper
    return decorator

@repeat(3)
def greet(name):
    print(f"Hello, {name}")

greet("World")

8. Monkey Patching

Monkey patching dynamically modifies modules or classes at runtime.

import math

def new_sin(x):
    return math.cos(x)

math.sin = new_sin
print(math.sin(0))  # 1.0

9. Asyncio and Coroutines

asyncio enables asynchronous programming using an event loop and coroutines defined with async def.

import asyncio

async def fetch_data():
    await asyncio.sleep(1)
    return "Data"

async def main():
    data = await fetch_data()
    print(data)

asyncio.run(main())

10. Functional Programming: map, filter, reduce

Python supports functional patterns through map, filter, and reduce, often combined with lambdas.

from functools import reduce

nums = [1, 2, 3, 4]
evens = filter(lambda x: x % 2 == 0, nums)
squared = map(lambda x: x**2, evens)
product = reduce(lambda x, y: x * y, squared)

print(product)  # 64