Top 30 Python Interview Questions & Answers 2026

Python is a high-level, interpreted, general-purpose programming language created by Guido van Rossum in 1991. It's popular because: readable syntax (like English), huge library ecosystem (400K+ packages), versatile (web, AI, data, automation), strong community, and it's the #1 language on TIOBE Index 2026.

Interpreted (no compilation), dynamically typed (no type declarations), object-oriented, extensive standard library, cross-platform, garbage collected (automatic memory management), supports multiple paradigms (OOP, functional, procedural).

List: mutable (can change), uses [], slightly slower. Tuple: immutable (cannot change), uses (), faster, can be used as dictionary keys. Use tuples for fixed data (coordinates, RGB), lists for collections that change.

Python uses: reference counting (tracks how many variables point to an object), garbage collector (cleans cyclic references), private heap (all objects stored here), memory pool (small object allocation). The gc module handles automatic cleanup.

*args allows a function to accept any number of positional arguments (as a tuple). **kwargs allows any number of keyword arguments (as a dictionary). Example: def func(*args, **kwargs) can be called as func(1, 2, name="Rahul").

A decorator is a function that wraps another function to add behavior without modifying it. Uses @syntax. Example: @login_required in Django checks authentication before running the view function. Common in Flask (@app.route), logging, caching.

List comprehension creates lists in one line: [expression for item in iterable if condition]. Example: squares = [x**2 for x in range(10) if x % 2 == 0] gives [0, 4, 16, 36, 64]. It's faster and more Pythonic than equivalent for loops.

== checks value equality (do they have the same content?). is checks identity (are they the same object in memory?). Example: a = [1,2]; b = [1,2]; a == b is True, but a is b is False (different objects with same value).

Generators produce values lazily using yield — one at a time, on demand. Lists store all values in memory at once. Generators are memory-efficient for large datasets. Example: (x**2 for x in range(1000000)) uses almost no memory vs [x**2 for x in range(1000000)].

GIL is a mutex that allows only one thread to execute Python bytecode at a time. This means Python threads can't achieve true parallelism for CPU-bound tasks. Workarounds: multiprocessing module, asyncio for I/O-bound tasks, or using C extensions.

Shallow copy (copy.copy()) creates a new object but references the same nested objects. Deep copy (copy.deepcopy()) creates completely independent copies of all nested objects. Matters when you have lists inside lists or objects inside objects.

Using try/except/else/finally blocks. try: risky code. except SpecificError: handle it. else: runs if no error. finally: always runs (cleanup). Best practice: catch specific exceptions, never bare except:. Use raise to throw custom exceptions.

__init__ is the constructor method — called automatically when creating an object. self refers to the current instance of the class. self.name = name stores the parameter as an instance attribute. Every method's first parameter must be self.

Inheritance lets a child class reuse code from a parent class. class Child(Parent): inherits all methods/attributes. Use super().__init__() to call parent constructor. Python supports multiple inheritance: class C(A, B). MRO (Method Resolution Order) determines which method is called.

append(x) adds x as a single element (even if x is a list). extend(iterable) adds each element of the iterable individually. Example: [1,2].append([3,4]) → [1,2,[3,4]]. [1,2].extend([3,4]) → [1,2,3,4].

Multiple ways: 1) Slicing: s[::-1] (most Pythonic). 2) reversed(): "".join(reversed(s)). 3) Loop: build new string character by character. The slicing method is fastest and most commonly expected in interviews.

Numeric: int, float, complex. Sequence: str, list, tuple, range. Set: set, frozenset. Mapping: dict. Boolean: bool. Binary: bytes, bytearray, memoryview. None: NoneType. Most used in interviews: list, dict, tuple, set, str.

A small anonymous function defined with lambda keyword. Syntax: lambda args: expression. Example: square = lambda x: x**2. Used with map(), filter(), sorted(). Limited to single expression — for complex logic, use regular def functions.

map(func, iterable): applies func to every item, returns iterator. filter(func, iterable): keeps items where func returns True. reduce(func, iterable): combines all items into one value (from functools). Example: list(map(lambda x: x*2, [1,2,3])) → [2,4,6].

PEP 8 is Python's official style guide. Key rules: 4 spaces for indentation, max 79 chars per line, snake_case for functions/variables, CamelCase for classes, 2 blank lines between top-level definitions. Following PEP 8 makes code readable and professional.

Python: interpreted, dynamically typed, concise syntax, indentation-based blocks. Java: compiled to bytecode, statically typed, verbose syntax, curly-brace blocks. Python is faster to write; Java is faster to execute. Python for AI/scripting; Java for enterprise/Android.

A virtual environment is an isolated Python installation for a specific project. It prevents package conflicts between projects. Create: python -m venv myenv. Activate: source myenv/bin/activate (Linux) or myenv\Scripts\activate (Windows). Use requirements.txt to share dependencies.

Use open(filename, mode) with with statement. Modes: r (read), w (write/overwrite), a (append), rb (read binary). with open("file.txt", "r") as f: content = f.read(). The with statement auto-closes the file. Always prefer with over manual open/close.

Module: a single .py file containing functions/classes. Package: a directory containing multiple modules + an __init__.py file. Example: math is a module, numpy is a package (contains many modules like numpy.random, numpy.linalg).

def is_prime(n): if n < 2: return False; for i in range(2, int(n**0.5)+1): if n%i==0: return False; return True. Key optimization: only check up to √n (not n). Time complexity: O(√n). This is the standard interview answer.

Methods with double underscores: __init__, __str__, __repr__, __len__, __add__, __eq__, etc. They let you define how objects behave with operators and built-in functions. __str__ defines print() output, __len__ defines len() behavior, __add__ defines + operator.

Python dicts use hash tables internally. Keys are hashed to determine storage location. This gives O(1) average lookup/insert/delete time. Keys must be hashable (immutable): strings, numbers, tuples. Since Python 3.7, dicts maintain insertion order.

asyncio is Python's library for asynchronous programming. It uses async/await syntax to handle I/O-bound tasks concurrently without threads. Perfect for: web scraping, API calls, database queries, file I/O. Not useful for CPU-bound tasks (use multiprocessing instead).

1) Profile first (cProfile). 2) Use built-in functions (they're C-optimized). 3) List comprehensions over loops. 4) Use generators for large data. 5) Cache with @lru_cache. 6) Use NumPy for numerical operations. 7) Consider PyPy or Cython for critical sections.

Thread: lightweight, shares memory, limited by GIL (no true parallelism for CPU tasks). Process: heavyweight, separate memory, true parallelism. Use threading for I/O-bound tasks (network, file). Use multiprocessing for CPU-bound tasks (calculations, data processing).