Python Interview Questions: The Complete Prep Guide for 2026

Python is tested in interviews across an enormous range of roles: backend engineering, data engineering, machine learning, DevOps scripting, and automation. The good news is that the core Python topics tested are consistent. The same questions about generators, decorators, the GIL, and object-oriented patterns come up whether you are interviewing at a startup or a large tech company.

This guide covers the Python concepts most frequently tested in technical interviews, with explanations and examples designed to help you give clear, confident answers, not just recall definitions.

1. Python Data Structures: Lists, Tuples, Sets, and Dicts

Interviewers often start with "what is the difference between a list and a tuple?" The key distinctions go beyond mutability:

List, mutable, ordered, allows duplicates, O(n) search, supports all sequence operations.
Tuple, immutable, ordered, hashable (so usable as dict keys and set members), slightly faster for iteration.
Set, mutable, unordered, no duplicates, O(1) average lookup. frozenset is immutable.
Dict, mutable key-value mapping, O(1) average lookup and insert, ordered by insertion since Python 3.7.

A common follow-up: "When would you use a set over a list?" Answer: membership testing (x in s) on a set is O(1); on a list it is O(n). If you are checking membership frequently against a large collection, convert to a set first.

2. Generators and Iterators

Generators are one of Python's most powerful features and come up regularly in interviews for data and backend roles.

python

# Regular function, builds entire list in memory
def get_squares(n):
    return [x ** 2 for x in range(n)]

# Generator function, yields one value at a time
def gen_squares(n):
    for x in range(n):
        yield x ** 2

# Generator expression (lazy list comprehension)
squares = (x ** 2 for x in range(1_000_000))

# All three produce the same values, but the generator
# uses O(1) memory regardless of n.

Generators implement the iterator protocol automatically, they have__iter__ and __next__ methods. The yield keyword suspends the function, saves its state, and resumes where it left off on the next call to next().

3. Decorators

A decorator is a higher-order function: it accepts a function as input, wraps it with additional behavior, and returns the modified function. Python's @ syntax is syntactic sugar.

python

import functools
import time

def timer(func):
    @functools.wraps(func)   # preserves func's __name__ and __doc__
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        elapsed = time.perf_counter() - start
        print(f"{func.__name__} ran in {elapsed:.4f}s")
        return result
    return wrapper

@timer
def slow_operation():
    time.sleep(0.1)
    return "done"

slow_operation()  # slow_operation ran in 0.1001s

Always use @functools.wraps(func) inside your decorator. Without it, the wrapped function loses its original __name__, __doc__, and other attributes , which breaks introspection tools and documentation generators.

4. Object-Oriented Python: Dunder Methods and MRO

Interviewers regularly ask about Python's data model, specifically dunder (double underscore) methods that customize class behavior.

__init__, called when an instance is created (not the constructor, that's __new__).
__repr__, unambiguous developer representation; used in the REPL.
__str__, human-readable representation; used by print().
__len__, __getitem__, make your class behave like a sequence.
__enter__ / __exit__, implement the context manager protocol.
__eq__, __hash__, equality and hashing (always define both together).

For multiple inheritance, Python uses the C3 linearization algorithm (Method Resolution Order, MRO) to determine which method to call. Use ClassName.__mro__ or help(ClassName) to inspect the resolution order.

5. Concurrency: Threading, Multiprocessing, and asyncio

This topic distinguishes intermediate from senior Python candidates.

threading, shares memory, uses OS threads, limited by the GIL for CPU-bound work but effective for I/O-bound tasks.
multiprocessing, spawns separate Python processes each with their own GIL, enabling true CPU parallelism at the cost of higher memory use and IPC overhead.
asyncio, single-threaded cooperative concurrency via event loop and async/await; ideal for high-concurrency I/O-bound workloads (web servers, database clients, HTTP calls).

The GIL (Global Interpreter Lock) in CPython prevents multiple threads from executing Python bytecode simultaneously. For CPU-bound work, use multiprocessing or offload to C extensions. For I/O-bound work, all three approaches are effective, asyncio is typically most efficient at scale.

6. Most Common Python Interview Questions

What is the difference between is and == in Python?
What are mutable and immutable types? Give examples.
How does Python manage memory? (Reference counting + cyclic GC.)
What is a list comprehension? How does it differ from a generator expression?
What is the difference between @staticmethod and @classmethod?
Explain Python's GIL. When does it matter?
What is the difference between deepcopy and shallow copy?
How does with work? Write a class that supports the context manager protocol.