Top 10 PythonInterview Questions & Answers (2025)

Python is a high-level, interpreted, dynamically-typed language known for its readable syntax. Its popularity comes from: a vast standard library and ecosystem (NumPy, Pandas, Django, Flask), strong community, versatility across web development, data science, ML, automation, and scripting, and the fact that it reads almost like plain English, lowering the learning curve.

A decorator is a function that wraps another function to extend its behaviour without modifying it. The @decorator syntax is shorthand for func = decorator(func). Common uses: logging, authentication checks (@login_required in Django), caching (@functools.lru_cache), rate limiting, and timing function execution. Decorators are a powerful application of Python's first-class functions and closures.

Lists are ordered, mutable, and allow duplicates — use for sequences you'll modify. Tuples are ordered, immutable, and allow duplicates — use for fixed records, dict keys, or when you want to signal that data shouldn't change. Sets are unordered, mutable, and contain no duplicates — use for membership tests (O(1) lookup), deduplication, and set operations (union, intersection).

A generator is a function that uses yield instead of return to produce a sequence of values lazily — one at a time, on demand. Calling a generator function returns a generator object; values are computed only when iterated. This makes generators memory-efficient for large datasets. Generator expressions ((x*2 for x in range(n))) are like lazy list comprehensions.

The GIL is a mutex in CPython that allows only one thread to execute Python bytecode at a time, even on multi-core systems. This means Python threads don't truly run in parallel for CPU-bound work. Workarounds: use multiprocessing (separate processes, each with their own GIL) for CPU-bound tasks, or use async/await (asyncio) for I/O-bound concurrency. NumPy operations release the GIL.

List comprehensions provide a concise way to create lists: [expression for item in iterable if condition]. They are faster than equivalent for loops (optimised in CPython) and more readable. dict comprehensions ({k: v for k, v in items}), set comprehensions ({x for x in iterable}), and generator expressions ((x for x in iterable)) follow the same syntax.

*args collects additional positional arguments into a tuple. **kwargs collects additional keyword arguments into a dictionary. They let functions accept variable numbers of arguments. The names args and kwargs are conventions — the * and ** are the actual syntax. Order in function signatures must be: positional, *args, keyword-only, **kwargs.

Dunder methods (__init__, __str__, __repr__, __len__, __getitem__, __eq__, __lt__, etc.) define how objects interact with Python's built-in operations. __init__ is the constructor. __str__ defines str(obj). __repr__ defines repr(obj) for debugging. __len__ enables len(obj). __getitem__ enables obj[key]. Implementing these makes your classes feel like native Python types.

A shallow copy (copy.copy()) creates a new object but inserts references to the original's nested objects — mutating a nested list in the copy affects the original. A deep copy (copy.deepcopy()) recursively copies all nested objects, creating fully independent duplicates. For simple objects with no nesting, both behave the same.

A context manager handles setup and teardown automatically. The with statement calls __enter__ on entry and __exit__ on exit (even if an exception occurs). The most common use is file handling: with open('file') as f — the file is guaranteed to close after the block. You can create custom context managers using the contextlib.contextmanager decorator.