Top 50 Python Interview Questions and Answers for 2026

Question

1. What are Python’s key features?

Answer 1

Python is a high-level, interpreted programming language known for: - Easy syntax and readability - Dynamic typing - Automatic memory management (garbage collection) - Extensive standard library - Support for multiple programming paradigms: procedural, object-oriented, and functional - Interpreted language; portable across platforms - Large ecosystem and community support

Answer 2

PEP 8 is Python’s official style guide. It provides conventions on code layout, indentation, naming conventions, line length, and best practices to write readable and consistent Python code.

Answer 3

Common built-in data types include: - Numeric types: int, float, complex - Boolean: bool - Sequence types: list, tuple, range - Text type: str - Set types: set, frozenset - Mapping type: dict - None type: NoneType

Answer 4

Collections include data structures like: - list: ordered, mutable sequences - tuple: ordered, immutable sequences - set: unordered unique elements - dict: key-value pairs (hash map) - Specialized containers from collections module: defaultdict, Counter, OrderedDict, named tuples, etc.

Answer 5

Python manages memory automatically using: - Reference counting for most objects - Garbage collection that detects and handles cyclic references - Memory allocation handled by Python’s private heap space

Answer 6

Python supports Object-Oriented Programming by: - Classes and objects - Inheritance (single and multiple) - Encapsulation with private and protected members (by convention) - Polymorphism through method overriding - Special methods (like __init__, __str__) - Support for properties and descriptors

Answer 7

List comprehensions provide a concise way to create lists:

squares = [x * x for x in range(5)]

Equivalent to:

squares = []
for x in range(5):
    squares.append(x * x)

They can include conditions and nested loops.

Answer 8

Python handles exceptions with try, except, else, and finally blocks: - try: block to test for errors - except: block to handle exceptions - else: executes if no exception - finally: executes always, good for cleanup

Answer 9

Python code is executed line-by-line by the Python interpreter, which converts code to bytecode and runs it on the Python Virtual Machine (PVM), rather than compiling directly to machine code before execution.

Answer 10

Decorators are functions that modify the behavior of other functions or methods. They wrap target functions to extend functionality without changing the original code. Example:

def decorator(func):
    def wrapper():
        print("Before call")
        func()
        print("After call")
    return wrapper

@decorator
def greet():
    print("Hello!")

greet()

Answer 11

Lists are mutable sequences, allowing modification. Tuples are immutable and generally faster.

Answer 12

Generators produce items one at a time using yield, helping save memory for large sequences.

Answer 13

It uses reference counting and cyclic garbage collector to free memory occupied by unreferenced objects.

Answer 14

- Shallow copy copies the object but references nested objects. - Deep copy duplicates nested objects as well.

Answer 15

Using pip for installations, virtualenv or venv for isolated environments.

Answer 16

GIL allows only one thread to execute Python bytecode at a time, affecting concurrency in multi-threaded programs.

Answer 17

Python 3 is the present and future with new syntax and features; Python 2 is deprecated. Key differences include print function, integer division, Unicode handling.

Answer 18

Using open(), file methods like read(), write(), and best with with statement for safe resource management.

Answer 19

Anonymous functions defined with lambda for short, inline function expressions.

Answer 20

Both can filter items; filter() uses a function returning boolean, list comprehensions use an inline if clause. Example:

filtered = list(filter(lambda x: x % 2 == 0, numbers))
filtered = [x for x in numbers if x % 2 == 0]

Answer 21

The with statement simplifies exception handling by encapsulating common preparation and cleanup tasks, like opening and automatically closing files. Example:

with open("file.txt", "r") as f:
    data = f.read()
# File is automatically closed here

Answer 22

- list: Can hold heterogeneous data types; versatile and part of core Python. - array.array: Stores homogeneous data types and is more memory-efficient. - Use array when working with large numeric data for performance gains.

Answer 23

- *args: Allows a function to accept any number of positional arguments as a tuple. - **kwargs: Accepts keyword arguments as a dictionary. Example:

def func(*args, **kwargs):
    print(args)
    print(kwargs)

func(1, 2, a=3, b=4)

Answer 24

Built-in data structures include: - list, tuple, set, frozenset, dict, and range. - Each has specific use cases regarding mutability, order, and performance.

Answer 25

- Use built-in functions and libraries. - Avoid global variables and reduce function call overhead. - Use list comprehensions and generator expressions. - Employ modules like numpy for heavy computations. - Profile the code to find bottlenecks. - Consider PyPy or Cython for speedups.

Answer 26

Metaclasses define the behavior of classes themselves — they are "classes of classes". You can customize class creation by defining or inheriting metaclasses. Useful for framework developers or advanced class customization.

Answer 27

Python supports multithreading via the threading module, but due to the GIL (Global Interpreter Lock), only one thread executes Python bytecode at a time. Best suited for I/O-bound tasks, not CPU-bound parallelism.

Answer 28

Python uses asyncio, async, and await syntax to write concurrent code using event loops without multithreading. It excels in I/O-bound and high-level structured network code.

Answer 29

Context managers manage setup and teardown actions via with statements, e.g., open/close files, acquire/release locks. Implemented using __enter__ and __exit__ methods or the contextlib module.

Answer 30

Modules include: - unittest: Built-in framework similar to JUnit. - doctest: Tests embedded in docstrings. - External: pytest (popular, powerful third-party).

Answer 31

Use the built-in pdb debugger for breakpoints, stepping, and inspection. Integrate IDE debuggers, logging, or print statements for tracing.

Answer 32

Monkey patching means dynamically modifying or extending a module or class at runtime, e.g., changing method implementations. Use cautiously — can lead to maintenance challenges.

Answer 33

Slicing extracts parts of lists (or sequences) using syntax:

list[start:stop:step]

Returns a new list with elements from start to stop - 1, stepping by step.

Answer 34

They provide powerful pattern matching and text processing through the re module. Used for validation, searching, substitution, and parsing tasks.

Answer 35

By using try, except, else, and finally blocks to catch and manage exceptions gracefully.

Answer 36

Isolated spaces for Python projects that keep dependencies separate using tools like venv or virtualenv.

Answer 37

Write docstrings inside functions, classes, and modules using triple quotes. Tools like Sphinx generate documentation.

Answer 38

- Iterable: Objects you can iterate over (implement __iter__). - Iterator: Objects that produce items one at a time (implement __next__).

Answer 39

map() applies a function to every item of an iterable. Example:

result = list(map(lambda x: x*2, [1, 2, 3]))
print(result)

Answer 40

- is: Checks identity (are two objects the same instance). - ==: Checks equality (do two objects have the same value).

Answer 41

- Local: Defined within a function. - Global: Defined at the module or script top-level. - Nonlocal: Used in nested functions to refer to enclosing function's variables.

Answer 42

Modules are Python files (.py) containing functions, classes, variables. Import them using import or from ... import ... statements.

Answer 43

Comprehensions provide concise syntax for creating sequences like lists, sets, or dictionaries. Example list comprehension:

squares = [x*x for x in range(5)]

Answer 44

Use the json module to encode (dump) and decode (load) JSON data. Example:

import json

data = json.loads(json_string)
json_str = json.dumps(data)

Answer 45

Functions like len(), print(), range(), sum(), max(), min(), sorted(), and many more are available by default.

Answer 46

- Mutable: Objects that can change after creation (e.g., list, dict, set). - Immutable: Objects that cannot be changed (e.g., int, str, tuple).

Answer 47

- Use built-in functions like map() or comprehensions. - Avoid redundant calculations inside loops. - Minimize attribute lookups. - Use local variables. - For heavy computation, consider libraries like numpy.

Answer 48

pass is a null statement used as a placeholder in empty code blocks (like loops, functions) where code will be added later.

Answer 49

Common libraries include: - requests for HTTP - numpy, pandas for data analysis - flask, Django for web development - pytest for testing - matplotlib, seaborn for visualization

Answer 50

Use the datetime module for creating, manipulating, formatting, and parsing dates and times. Example:

from datetime import datetime

now = datetime.now()
print(now.strftime("%Y-%m-%d %H:%M:%S"))