Python Interview Questions: 20 Questions Every Developer Must Know in 2026

Why Python Interviews Are Unique

Python is the most popular programming language in 2026, used in web development, data science, AI/ML, automation, and DevOps. But Python interviews are tricky — companies don't just test if you can write Python. They test if you understand Python's unique features: its memory model, GIL, decorators, generators, and Pythonic idioms.

Here are the 20 questions you're most likely to face, with answers that will impress any interviewer.

Core Python (Questions 1-7)

1. What is the difference between a list and a tuple?

Lists are mutable (can be modified after creation) and use square brackets []. Tuples are immutable and use parentheses (). Tuples are faster, use less memory, and can be used as dictionary keys. Use tuples for fixed collections and lists when you need to modify data.

2. Explain Python's GIL (Global Interpreter Lock).

The GIL is a mutex that allows only one thread to execute Python bytecode at a time in CPython. This means CPU-bound tasks don't benefit from threading — use multiprocessing instead. I/O-bound tasks (network calls, file I/O) still benefit from threading because the GIL is released during I/O operations.

3. What are *args and **kwargs?

*args allows a function to accept any number of positional arguments as a tuple. **kwargs accepts any number of keyword arguments as a dictionary. They're used for flexible function signatures and method forwarding in decorators and inheritance.

4. How does Python's memory management work?

Python uses reference counting as the primary mechanism — each object tracks how many references point to it. When the count drops to zero, memory is freed. A cyclic garbage collector handles circular references. Python also uses memory pools (via pymalloc) for small objects.

5. What is the difference between shallow copy and deep copy?

Shallow copy (copy.copy()) creates a new object but references the same nested objects. Deep copy (copy.deepcopy()) creates a completely independent copy of the object and all nested objects. This distinction matters when working with nested lists, dictionaries, or custom objects.

6. Explain list comprehension vs generator expression.

List comprehensions [x for x in range(n)] create the entire list in memory. Generator expressions (x for x in range(n)) produce values lazily, one at a time. Generators are memory-efficient for large datasets because they don't store all values simultaneously.

7. What are Python decorators and how do they work?

Decorators are functions that modify the behavior of other functions. They use the @decorator syntax and work by wrapping the original function. Common use cases: logging, authentication, caching (@lru_cache), and rate limiting. Under the hood, @decorator is equivalent to func = decorator(func).

OOP & Design (Questions 8-13)

8. Explain Python's MRO (Method Resolution Order).

MRO determines the order in which base classes are searched when looking for a method. Python uses the C3 linearization algorithm. You can inspect it with ClassName.__mro__ or ClassName.mro(). This is crucial in multiple inheritance scenarios.

9. What are __init__, __new__, and __call__?

__new__ creates a new instance (rarely overridden, used in singletons and metaclasses). __init__ initializes the instance. __call__ makes an instance callable like a function. Together they control the complete lifecycle of object creation and usage.

10. What is the difference between @staticmethod and @classmethod?

@staticmethod doesn't receive any implicit first argument — it's essentially a regular function inside a class. @classmethod receives the class (cls) as the first argument, making it useful for factory methods and alternative constructors.

11. Explain Python's dunder (magic) methods.

Dunder methods like __str__, __repr__, __eq__, __hash__, __len__, and __iter__ let you define how your objects behave with built-in operations. They enable operator overloading, iteration protocols, and context manager behaviors.

12. What are Abstract Base Classes (ABCs)?

ABCs (from abc module) define interfaces that subclasses must implement. Use @abstractmethod to declare required methods. Trying to instantiate a class with unimplemented abstract methods raises TypeError. ABCs enforce contracts in large codebases.

13. Explain the descriptor protocol.

Descriptors are objects that define __get__, __set__, or __delete__ methods. They power Python's properties, class methods, and static methods behind the scenes. Understanding descriptors helps you build powerful, reusable attribute management patterns.

Advanced Python (Questions 14-20)

14. What are context managers and how do you create one?

Context managers handle setup/teardown via with statements. Create them with a class implementing __enter__ and __exit__, or use the @contextmanager decorator from contextlib for a simpler generator-based approach.

15. Explain async/await in Python.

async def creates a coroutine. await pauses execution until the awaited coroutine completes. asyncio.run() starts the event loop. Async is ideal for I/O-bound operations (HTTP requests, database queries) where you need concurrency without threads.

16. What is a metaclass?

A metaclass is the "class of a class" — it controls how classes are created. The default metaclass is type. Custom metaclasses can modify class creation, enforce coding standards, register classes automatically, or implement ORMs (like Django models).

17. How do you handle exceptions in Python?

Use try/except/else/finally. Best practices: catch specific exceptions, never use bare except:, use else for code that runs only if no exception occurred, and finally for cleanup. Create custom exceptions by subclassing Exception.

18. What is the walrus operator (:=)?

The walrus operator (introduced in Python 3.8) assigns values as part of an expression. Example: if (n := len(data)) > 10: assigns len(data) to n and checks the condition in one line. Useful in while loops, comprehensions, and conditional logic.

19. Explain Python's type hints and their benefits.

Type hints (def greet(name: str) -> str:) provide static type information without runtime enforcement. Benefits: better IDE support, catch bugs early with mypy, serve as documentation, and improve code readability. Python 3.12+ supports even more expressive type syntax.

20. What are dataclasses and when should you use them?

@dataclass (from Python 3.7+) auto-generates __init__, __repr__, __eq__, and more. Use them for classes that primarily hold data. They reduce boilerplate and support features like default values, frozen instances, and slots for memory efficiency.

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