Practice data structures, algorithms, decorators, and Python-specific patterns with coding challenges
Start Practicing NowMaster lists, dicts, sets, comprehensions, and built-in functions
Understand classes, inheritance, decorators, and metaclasses
Learn async/await, asyncio, and concurrent programming patterns
Select Python as your interview topic and customize the difficulty level
Answer realistic Python interview questions in a simulated environment
Receive detailed feedback on your answers, including areas to improve
Monitor your improvement and identify strengths and weaknesses
Data structures (lists, dicts, sets, tuples)
List comprehensions and generators
Decorators and context managers
Object-oriented programming concepts
Async/await and asyncio
Python standard library and common packages
A: Decorators are functions that modify the behavior of other functions. They use the @ syntax. Example: @decorator above a function definition. Common use cases: @property for getters/setters, @staticmethod, @classmethod. You can create custom decorators: def my_decorator(func): def wrapper(*args, **kwargs): # do something; return func(*args, **kwargs); return wrapper
A: Lists are mutable, ordered collections: [1,2,3]. Tuples are immutable, ordered: (1,2,3). Sets are mutable, unordered, unique elements: {1,2,3}. Use lists for ordered collections you'll modify, tuples for fixed data (dict keys, multiple return values), sets for uniqueness and membership testing (O(1) lookup).
A: Context managers handle setup and teardown of resources automatically. The 'with' statement ensures cleanup happens even if exceptions occur. Example: with open('file.txt') as f: # file automatically closes. You can create custom context managers using __enter__ and __exit__ methods or the contextlib module.
A: The Global Interpreter Lock (GIL) allows only one thread to execute Python bytecode at a time. This means multithreading in Python doesn't achieve true parallelism for CPU-bound tasks. For CPU-intensive work, use multiprocessing instead. Threading still works well for I/O-bound operations where threads spend time waiting.
Master list/dict comprehensions and generator expressions for clean, efficient code
Understand *args and **kwargs for flexible function arguments
Know the difference between shallow and deep copy
Practice with Python's standard library: collections, itertools, functools
Understand duck typing, EAFP (Easier to Ask Forgiveness than Permission) principle
Join thousands of developers who have improved their interview skills with Vibe Interviews
Start Your Python Interview Practice