🐍 Python Interview Questions

Master Python interviews with questions on core Python, OOP, decorators, async programming, and frameworks

Data structures, decorators, generators, async/await, OOP, and Python fundamentals

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15-Minute Python Cheatsheet

Quick reference for last-minute interview preparation

📦 Data Structures

list - Mutable, ordered, O(1) append, O(n) insert
tuple - Immutable, ordered, hashable
set - Unique items, O(1) lookup
dict - Key-value pairs, O(1) access
deque - O(1) append/pop both ends

🔑 Key Concepts

GIL: Only 1 thread runs Python at a time
Mutable default args: Avoid def f(x=[])
is vs ==: Identity vs equality
Pass by assignment: Not by value/reference
Shallow vs Deep copy: copy() vs deepcopy()

🎨 Decorators & Generators

Decorator: Function that wraps another function
@functools.wraps(func) - Preserve metadata
Generator: Lazy evaluation with yield
(x for x in range(n)) - Generator expression
Memory efficient for large datasets

🏗️ OOP Essentials

@property - Getter/setter pattern
@classmethod - Access class, not instance
@staticmethod - No access to class/instance
__slots__ - Memory optimization
MRO: Method Resolution Order (C3 linearization)

🔐 Context Managers

with statement ensures cleanup
__enter__ and __exit__ methods
@contextmanager decorator shortcut
Use for files, locks, DB connections
Exception handling in __exit__

⚡ Async & Concurrency

async/await - Non-blocking I/O
asyncio.gather() - Concurrent tasks
threading - I/O-bound tasks
multiprocessing - CPU-bound (bypass GIL)
concurrent.futures - High-level API

⚠️ Common Interview Gotchas

• Mutable default arguments persist across calls
• List comprehensions create new scope (Python 3)
• range() is a lazy sequence, not a list
• Division: / returns float, // returns int
• *args is tuple, **kwargs is dict
• Late binding in closures (loop variable issue)

Python has four main built-in data structures, each with different characteristics and use cases.

  • List: Ordered, mutable, allows duplicates. Use for sequences that change.
  • Tuple: Ordered, immutable, allows duplicates. Use for fixed data.
  • Set: Unordered, mutable, no duplicates. Use for unique items and set operations.
  • Dictionary: Key-value pairs, mutable, keys must be unique. Use for lookups.
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Decorators are a powerful feature that allows you to modify the behavior of functions or classes. They are functions that take another function as input and extend its behavior without explicitly modifying it.

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Generators are a simple way to create iterators. They use yield instead of returnand automatically implement the iterator protocol, making them memory-efficient for large datasets.

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*args allows a function to accept any number of positional arguments as a tuple.**kwargs allows a function to accept any number of keyword arguments as a dictionary.

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Context managers allow you to allocate and release resources precisely when you want to. The with statement ensures proper acquisition and release of resources.

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Magic methods (also called dunder methods) are special methods with double underscores that allow you to define how objects behave with built-in operations. They enable operator overloading and customize object behavior.

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Async/await enables asynchronous programming, allowing you to write concurrent code that can handle multiple I/O operations efficiently without blocking. It's particularly useful for network requests, file I/O, and database operations.

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The GIL is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecode simultaneously. This makes CPU-bound multithreaded programs slower but doesn't affect I/O-bound programs.

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List comprehensions create a complete list in memory, while generator expressions create an iterator that generates values on demand. Generator expressions are more memory-efficient for large datasets.

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Python uses automatic memory management with reference counting and a cyclic garbage collector. Understanding this helps write efficient code and avoid memory leaks.

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Interview Tips for Python