Top 50 Python Interview Questions and Answers

Python has become one of the most widely used and versatile programming languages in the tech world today. From web development and automation to data science and machine learning, Python’s clean syntax, extensive libraries, and strong community support have made it the first choice for developers and engineers across domains.

Contents

Top 50 Python Interview Questions and Answers Basic-Level Python Interview Questions (1–15)Intermediate-Level Python Interview Questions (16–30)Advanced-Level Python Interview Questions (31–40)Scenario-Based Python Interview Questions (41–50)Core Concepts to Revise Before a Python Interview Conclusion

Because of its popularity, Python is a core part of technical interviews for many roles whether you are applying for a software development job, a data analyst role, or even a backend engineering position. Interviewers want to assess not just your knowledge of Python syntax, but also how well you can apply it to solve real-world problems.

This blog brings you a curated list of 50 Python interview questions and answers, organized by difficulty level. Whether you are a fresher preparing for your first interview or a professional brushing up before a coding round, this guide will help you revise key concepts, build confidence, and get ready to crack Python interviews with ease.

This blog is for anyone looking to strengthen their Python knowledge in preparation for a technical interview. It is especially useful for:

Freshers who want to understand common Python interview patterns and concepts
Backend developers who work with APIs, databases, or scripting
Data scientists or analysts using Python for data wrangling, visualization, or ML
QA engineers and automation testers using Python for test scripts or frameworks
Experienced developers who want to quickly revise Python before an interview round

Whether you are preparing for a coding test, a live technical round, or a take-home assignment, these questions will help you get interview-ready.

Basic-Level Python Interview Questions (1–15)

1. What are the key features of Python?

Answer:
Python is a high-level, interpreted programming language known for its readability and versatility. Its key features include:

Easy-to-read syntax
Dynamic typing and memory management
Large standard library
Cross-platform compatibility
Support for multiple programming paradigms (object-oriented, procedural, and functional)

2. What is the difference between a list and a tuple in Python?

Answer:

List: Mutable, meaning its elements can be changed. Defined using square brackets [ ].
Tuple: Immutable, meaning once defined, its elements cannot be changed. Defined using parentheses ( ).

3. What is the purpose of indentation in Python?

Answer:
Indentation is used to define code blocks in Python. Unlike other programming languages that use braces or keywords, Python relies on consistent indentation to structure its code. Improper indentation will result in a syntax error.

4. Explain the use of `is` versus `==` in Python.

Answer:

== checks for value equality (i.e., whether two variables have the same value).
is checks for object identity (i.e., whether two variables refer to the same memory location).

5. What are the basic built-in data types in Python?

Answer:
Python’s primary built-in data types include:

Numeric: int, float, complex
Sequence: list, tuple, range
Text: str
Set: set, frozenset
Mapping: dict
Boolean: bool
Binary: bytes, bytearray, memoryview

6. What is a dictionary in Python?

Answer:
A dictionary is an unordered collection of key-value pairs. Each key must be unique and immutable. Dictionaries are defined using curly braces {}.
Example:

pythonCopyEditperson = {"name": "John", "age": 28}

7. Differentiate between `append()` and `extend()` methods in a list.

Answer:

append() adds a single element to the end of the list.
extend() adds elements from an iterable (like a list or tuple) to the list.
Example:

a = [1, 2]  
a.append([3, 4])  # Output: [1, 2, [3, 4]]  
a.extend([5, 6])  # Output: [1, 2, [3, 4], 5, 6]

8. What are logical operators in Python?

Answer:
Python includes the following logical operators:

and: Returns True if both statements are true
or: Returns True if at least one statement is true
not: Reverses the logical state

9. How are functions defined in Python?

Answer:
Functions are defined using the def keyword followed by the function name and parentheses.
Example:

pythonCopyEditdef greet(name):  
    return "Hello, " + name

10. What is the difference between `None` and `False`?

Answer:

None represents the absence of a value or a null object.
False is a Boolean value.
While both evaluate to False in conditional expressions, they are not equivalent.

11. What are Python keywords?

Answer:
Keywords are reserved words in Python that have special meaning and cannot be used as variable names. Examples include if, for, while, def, class, try, return, etc.

12. What is the purpose of the `range()` function?

Answer:
The range() function generates a sequence of numbers. It is commonly used for iteration in loops.
Example:

for i in range(3):  
    print(i)  # Output: 0, 1, 2

13. How is exception handling implemented in Python?

Answer:
Python uses the try, except, else, and finally blocks to handle exceptions.
Example:

try:  
    result = 10 / 0  
except ZeroDivisionError:  
    print("Cannot divide by zero.")

14. What is the use of `pass`, `break`, and `continue` in loops?

Answer:

pass: A null statement, used as a placeholder.
break: Terminates the loop entirely.
continue: Skips the current iteration and moves to the next.

15. What is a string in Python and how is it declared?

Answer:
A string is a sequence of Unicode characters. It can be declared using single quotes ('text'), double quotes ("text"), or triple quotes for multi-line strings.
Example:

Editmessage = "Welcome to Python"

Intermediate-Level Python Interview Questions (16–30)

16. What is the difference between `deepcopy()` and `copy()` in Python?

Answer:

copy() creates a shallow copy of an object—modifications to nested objects will affect both copies.
deepcopy() creates a complete copy, including nested elements.
They are available via the copy module:

import copy  
shallow = copy.copy(obj)  
deep = copy.deepcopy(obj)

**17. What are `*args` and `kwargs` used for?

Answer:

*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.
They enhance flexibility in function definitions.
Example:

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

18. What is the difference between a module and a package in Python?

Answer:

A module is a single Python file containing functions, classes, or variables.
A package is a directory that contains multiple modules and an __init__.py file to mark it as a package.

19. Explain Python’s LEGB rule.

Answer:
LEGB is the rule Python follows to resolve variable names:

L: Local scope
E: Enclosing scope
G: Global scope
B: Built-in scope
Python searches for variables in this order.

20. What are list comprehensions in Python?

Answer:
List comprehensions provide a concise way to create lists using a single line of code.
Example:

squares = [x**2 for x in range(5)]

21. How is exception handling different from error suppression in Python?

Answer:
Exception handling (try/except) manages errors gracefully. Suppressing errors (e.g., using pass) can hide bugs and should be avoided unless explicitly required.

22. What are lambda functions?

Answer:
Lambda functions are anonymous, single-expression functions defined using the lambda keyword.
Example:

square = lambda x: x * x  
print(square(4))  # Output: 16

23. What is a Python generator? How is it different from a list?

Answer:
A generator yields items one at a time using the yield keyword. It is memory-efficient and lazy-evaluated, unlike lists which store all elements in memory.
Example:

def count_up_to(n):  
    for i in range(n):  
        yield i

24. What are Python’s built-in map, filter, and reduce functions?

Answer:

map() applies a function to all items in an iterable.
filter() filters items based on a condition.
reduce() (from functools) cumulatively applies a function to elements.
Example:

from functools import reduce  
reduce(lambda x, y: x + y, [1, 2, 3])  # Output: 6

25. What are Python decorators?

Answer:
Decorators are functions that modify the behavior of other functions or methods. They are often used for logging, access control, or memoization.
Example:

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

26. What is the purpose of the `init` method in Python classes?

Answer:
The __init__ method is the constructor for a Python class. It initializes object attributes when an instance is created.
Example:

class Person:  
    def __init__(self, name):  
        self.name = name

27. What is the difference between `@staticmethod` and `@classmethod`?

Answer:

@staticmethod: Does not access class or instance variables.
@classmethod: Takes the class (cls) as its first argument and can access class variables.
Used for different design purposes within a class.

28. What are Python’s data hiding and encapsulation principles?

Answer:
Encapsulation bundles data and methods within a class. Python uses name mangling (e.g., __variable) to suggest private attributes, though true access restrictions are not enforced.

29. How do you handle file operations in Python?

Answer:
Using the built-in open() function with context managers:

with open('file.txt', 'r') as f:  
    content = f.read()

30. What is the use of `enumerate()` in Python?

Answer:
enumerate() adds a counter to an iterable and returns index-item pairs.
Example:

for i, value in enumerate(['a', 'b', 'c']):  
    print(i, value)

Advanced-Level Python Interview Questions (31–40)

31. What is the Global Interpreter Lock (GIL) in Python?

Answer:
The Global Interpreter Lock (GIL) is a mutex that prevents multiple native threads from executing Python bytecodes simultaneously in CPython. This ensures thread safety but can limit the performance of CPU-bound multi-threaded programs. However, it does not affect I/O-bound operations or multiprocessing.

32. How does memory management work in Python?

Answer:
Python uses automatic memory management, which includes reference counting and garbage collection. When an object’s reference count drops to zero, it is deallocated. The garbage collector handles cyclic references by periodically cleaning up unused memory.

33. What is monkey patching in Python?

Answer:
Monkey patching is the practice of modifying or extending code at runtime, especially classes or modules. It is generally discouraged unless necessary for testing or specific overrides.
Example:

import math  
math.sqrt = lambda x: "patched"  
print(math.sqrt(4))  # Output: "patched"

34. What is the difference between `str` and `repr`?

Answer:

__str__ is intended to return a readable, user-friendly string representation of an object.
__repr__ is meant for developers and should return a string that, ideally, can recreate the object.
If __str__ is not defined, Python uses __repr__ as a fallback.

35. How do you implement multithreading in Python?

Answer:
Multithreading is implemented using the threading module. While Python threads are suitable for I/O-bound tasks, the GIL limits CPU-bound performance.
Example:

import threading  
t = threading.Thread(target=my_function)  
t.start()

36. How does Python handle multiprocessing?

Answer:
The multiprocessing module allows Python programs to run across multiple processors. Each process has its own memory space and avoids the GIL constraint, making it suitable for CPU-bound tasks.
Example:

from multiprocessing import Process  
p = Process(target=my_function)  
p.start()

37. What are metaclasses in Python?

Answer:
Metaclasses are classes of classes. They define how classes themselves behave. You can use metaclasses to control class creation, modify attributes, or enforce rules. By default, Python uses type as the metaclass.

38. What are Python descriptors?

Answer:
Descriptors are objects that manage the access to another object’s attributes through methods like __get__, __set__, and __delete__. They are used behind the scenes in property access and are a powerful feature of Python’s object model.

39. Explain the concept of a context manager in Python.

Answer:
A context manager handles the setup and teardown of resources. It is implemented using __enter__ and __exit__ methods and is most commonly used via the with statement for file handling, locks, or database connections.
Example:

with open('file.txt') as f:  
    data = f.read()

40. What is the difference between `isinstance()` and `type()`?

Answer:

type() returns the exact type of an object.
isinstance() checks if an object is an instance of a class or a subclass thereof.
isinstance() is preferred when inheritance is involved.

Scenario-Based Python Interview Questions (41–50)

41. Write a Python function to check if a given number is a prime.

Answer:

def is_prime(n):
    if n <= 1:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

This function efficiently checks for primality using trial division up to the square root of the number.

42. How would you remove duplicates from a list while maintaining the original order?

Answer:

def remove_duplicates(lst):
    seen = set()
    return [x for x in lst if not (x in seen or seen.add(x))]

This uses a set to track seen values and a list comprehension to preserve order.

43. Write a function that returns the factorial of a number using recursion.

Answer:

def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n - 1)

Recursion is suitable for small input sizes; iterative methods are better for larger inputs.

44. How would you handle a large CSV file that cannot be fully loaded into memory?

Answer:
Use Python’s built-in csv module with file streaming:

import csv

with open('large_file.csv', mode='r') as file:
    reader = csv.reader(file)
    for row in reader:
        process(row)  # Replace with actual logic

This ensures efficient memory usage by reading line by line.

45. Given a string, write a function to determine if it is a palindrome.

Answer:

def is_palindrome(s):
    s = s.lower().replace(" ", "")
    return s == s[::-1]

This function ignores casing and whitespace for a generalized check.

46. How would you count the frequency of each word in a text file?

Answer:

from collections import Counter

def word_count(filename):
    with open(filename, 'r') as file:
        text = file.read().lower().split()
        return Counter(text)

The Counter class makes this task simple and efficient.

47. How do you handle exceptions when reading and writing to a file?

Answer:

pythonCopyEdittry:
    with open('data.txt', 'r') as file:
        data = file.read()
except FileNotFoundError:
    print("File not found.")
except IOError:
    print("An I/O error occurred.")

Using try-except blocks ensures robust file handling.

48. How would you implement a custom iterator in Python?

Answer:

class CountDown:
    def __init__(self, start):
        self.current = start

    def __iter__(self):
        return self

    def __next__(self):
        if self.current <= 0:
            raise StopIteration
        self.current -= 1
        return self.current + 1

This class implements the iterator protocol with __iter__ and __next__.

49. Write a function that returns the nth Fibonacci number using memoization.

Answer:

from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

@lru_cache optimizes the recursive solution by caching intermediate results.

50. How would you sort a list of dictionaries by a specific key?

Answer:

data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 20}]
sorted_data = sorted(data, key=lambda x: x['age'])

The sorted() function with a lambda expression allows flexible key-based sorting.

Core Concepts to Revise Before a Python Interview

To perform well in a Python-focused interview, candidates must demonstrate both theoretical understanding and practical proficiency. Interviewers typically assess your familiarity with Python syntax, problem-solving skills, and your ability to write clean, maintainable code.

Below is a list of core concepts you should review thoroughly before your interview:

1. Data Types and Data Structures

Built-in types: int, float, str, bool, complex
Data structures: list, tuple, set, dict, frozenset
Type conversion and casting
Understanding mutability and immutability

2. Control Flow and Iteration

if, elif, else statements
for and while loops
break, continue, and pass statements
Loop control with range() and enumerate()

3. Functions and Scope

Defining and calling functions
Positional, keyword, default, and variable-length arguments (*args, **kwargs)
Return values and multiple returns
Local, global, and nonlocal scopes (LEGB rule)

4. Object-Oriented Programming (OOP)

Creating classes and objects
Constructors (__init__) and destructors (__del__)
Instance methods, class methods, static methods
Inheritance, polymorphism, and method overriding
Dunder (magic) methods like __str__, __repr__, __len__, __eq__

5. Error Handling

Using try, except, else, and finally
Common exceptions: IndexError, KeyError, ValueError, TypeError, etc.
Custom exceptions with user-defined classes

6. Modules and Packages

Importing built-in and user-defined modules
Creating and using packages
Understanding __init__.py
Key standard libraries: math, random, os, sys, datetime, collections

7. File Handling

Reading and writing files using open()
Context managers (with statement)
Reading line-by-line and working with file pointers
Handling file-related exceptions

8. Functional Programming Tools

Anonymous functions (lambda)
map(), filter(), reduce()
List, set, and dictionary comprehensions

9. Advanced Python Features

Iterators and generators
Decorators and context managers
Memory management and garbage collection
Global Interpreter Lock (GIL) and concurrency basics

10. Practical Problem-Solving

String manipulation and pattern matching (re module)
Working with dates and times
Sorting and searching algorithms
Efficient handling of large data (e.g., with csv, pandas for data science roles)

By reviewing these core areas and practicing with hands-on examples, you will be well-equipped to handle both conceptual questions and coding challenges during your interview. Focus not only on writing syntactically correct code but also on explaining your logic clearly and professionally.

Conclusion

Python continues to be one of the most in-demand programming languages across industries—from web development and automation to data science and artificial intelligence. Its simplicity, readability, and versatility make it a core skill that employers consistently look for in candidates.

Confidence in this language comes from both understanding the language and using it regularly to solve meaningful problems. Keep building, keep learning and approach your interviews as opportunities to showcase what you know.