Top 70 Python Interview Questions & Answers: Ultimate Guide 2025

Python continues to dominate technical interviews in 2025 due to its versatility and real-world applications. Companies assess not only your coding knowledge but also how you solve problems efficiently. This blog compiles the top 70 Python interview questions and answers that reflect the latest hiring trends. It covers practical coding tasks, logic-based problems, and advanced concepts used in real projects, helping you prepare for both fresher and experienced-level roles. 

In this guide, you'll read more about Python interview questions and answers for freshers, Python coding interview questions and answers, and Python interview questions and answers for experienced professionals. You’ll also explore advanced Python concepts, frameworks, scenario-based problems, and preparation tips to help you stand out in your next interview. 

Take your Python skills to the next level and unlock career opportunities in data science, AI, and more. Explore our Online Data Science Courses and start building your future today!

Python Interview Questions and Answers for Freshers 

If you’re new to programming and preparing for your first Python interview, it’s important to understand the language deeply, not just memorize syntax. Interviewers test your grasp of basic concepts, coding logic, and problem-solving ability. Below is a detailed list of Python interview questions and answers for freshers, explained in simple terms with examples. 

1. What is Python? 

Python is a high-level, interpreted programming language known for its simplicity and readability. It was created by Guido van Rossum and first released in 1991. Python emphasizes code clarity and lets you write programs with fewer lines than many other languages. 

Python supports multiple programming paradigms, including: 

• Procedural programming (writing functions) 
• Object-oriented programming (using classes and objects) 
• Functional programming (using higher-order functions like map, filter, and reduce) 

It’s used widely in data science, web development, machine learning, automation, and software testing. 

2. What are Python’s key features? 

Python offers features that make it a top choice among developers: 

• Simple and readable syntax: You can focus on solving problems instead of dealing with complex code structures. 
• Interpreted language: Code runs line by line, making debugging easier. 
• Dynamically typed: You don’t need to declare variable types. 
• Extensive standard library: Built-in modules like math, os, and datetime save time. 
• Cross-platform: Works on Windows, macOS, and Linux. 
• Open-source: Free to use and supported by a large community. 
• Object-oriented: Everything in Python is an object. 

3. What are Python data types? 

Python has several built-in data types used to represent values. 

Category	Data Types	Examples
Numeric	int, float, complex	a = 5, b = 2.5, c = 3+4j
Sequence	str, list, tuple, range	"hello", [1, 2], (3, 4), range(5)
Mapping	dict	{'name': 'Rahul', 'age': 25}
Set	set, frozenset	{1, 2, 3}, frozenset({4, 5})
Boolean	bool	True, False
Binary	bytes, bytearray	b'hello'

Python’s flexibility allows automatic type conversion where possible. 

4. What is the difference between mutable and immutable data types? 

Mutable objects can change after creation, while immutable objects cannot. 

Mutable	Immutable
List, Dictionary, Set	String, Tuple, Number
Can modify elements	Cannot modify elements
Example: a = [1, 2]; a.append(3)	Example: x = "hello"; x[0] = "y" (Error)

Mutable types are ideal when you need to update data, while immutable types are safer for data integrity. 

Also Read: Understand What Is Mutable And Immutable In Python 

5. What are variables in Python? 

Variables are containers that store data values. You don’t declare their type explicitly — Python infers it. 

Example: 

name = "Rahul" 
age = 25 
is_student = True 
  

Here, name is a string, age is an integer, and is_student is a boolean. 

Variables follow naming rules: 

• Must start with a letter or underscore. 
• Cannot start with a number. 
• Are case-sensitive. 

6. Explain indentation in Python. 

Python uses indentation (spaces) to define code blocks instead of braces {}. The standard indentation is 4 spaces. 

Example: 

if True: 
    print("Indented block") 
print("Outside block") 
  

Incorrect indentation raises an IndentationError. Consistent indentation improves readability and enforces structure. 

7. What are lists and tuples? 

Both lists and tuples store ordered collections of data, but they differ in mutability. 

Property	List	Tuple
Mutability	Mutable	Immutable
Syntax	[]	()
Speed	Slower	Faster
Use case	When data changes	When data remains constant

Examples: 

my_list = [1, 2, 3] 
my_tuple = (1, 2, 3) 

Lists are used for dynamic data, while tuples are preferred for fixed data. 

8. What is a dictionary in Python? 

A dictionary stores key-value pairs and allows fast lookups. 

Example: 

student = {"name": "Riya", "age": 21, "grade": "A"} 
print(student["name"])  

 

Output

Riya 

Dictionaries are mutable, unordered, and indexed by keys. Keys must be immutable types like strings, numbers, or tuples. 

9. What are conditional statements in Python? 

Conditional statements control program flow based on conditions. 

Example: 

x = 10 
if x > 0: 
    print("Positive") 
elif x == 0: 
    print("Zero") 
else: 
    print("Negative") 
  

Common keywords: 

• if 
• elif 
• else 

These help your code make decisions dynamically. 

10. What are loops in Python? 

Loops execute a block of code multiple times. 

For Loop Example: 

for i in range(3): 
    print(i) 

While Loop Example: 

count = 0 
while count < 3: 
    print(count) 
    count += 1 
  

You can use break to exit early or continue to skip iterations. 

Also Read: Python for Loop 

11. What are functions in Python? 

Functions are reusable blocks of code that perform specific tasks. 

Example: 

def add(a, b): 
    return a + b 
print(add(3, 4)) 

Output: 7 

Functions improve readability and reduce repetition. You can pass parameters, return values, or define default arguments: 

def greet(name="Guest"): 
    print(f"Hello, {name}")   

12. What are *args and kwargs? 

These allow flexible argument passing. 

• *args collects non-keyword arguments as a tuple. 
• **kwargs collects keyword arguments as a dictionary. 

Example: 

def details(*args, **kwargs): 
    print(args) 
    print(kwargs) 
details(10, 20, name="Riya", age=22) 

Output: 

(10, 20) 
{'name': 'Riya', 'age': 22} 

13. What is the difference between return and yield? 

• return ends the function and returns a value immediately. 
• yield turns a function into a generator that returns values one by one. 

Example: 

def squares(n): 
    for i in range(n): 
        yield i * i 

Generators are memory-efficient because they produce results only when needed. 

14. What are lambda functions? 

A lambda function is a one-line anonymous function defined using the keyword lambda. 

Example: 

square = lambda x: x ** 2 
print(square(4)) 

Output: 16 

They’re often used with map(), filter(), and reduce() for concise operations. 

15. What are Python modules and packages? 

A module is a Python file containing functions, classes, or variables. 

A package is a collection of modules organized in directories. 

Example: 

import math 
print(math.sqrt(16)) 

You can create custom modules by saving your functions in a .py file and importing them elsewhere. 

16. What is the difference between deep copy and shallow copy? 

Shallow Copy	Deep Copy
Copies only object references	Copies entire objects recursively
Changes reflect in original	Independent copy
Created using copy.copy()	Created using copy.deepcopy()

Example: 

import copy 
list1 = [[1, 2], [3, 4]] 
shallow = copy.copy(list1) 
deep = copy.deepcopy(list1) 

 

17. What are Python exceptions? 

Exceptions handle runtime errors gracefully. 

Example: 

try: 
    print(10 / 0) 
except ZeroDivisionError: 
    print("Cannot divide by zero") 
finally: 
    print("Operation complete") 

Common exceptions include: 

• ValueError 
• TypeError 
• FileNotFoundError 
• IndexError 

18. What are Python decorators? 

Decorators modify the behavior of a function without changing its code. 

Example: 

def greet(func): 
    def wrapper(): 
        print("Hello!") 
        func() 
    return wrapper 
 
@greet 
def say_name(): 
    print("Rahul") 
 
say_name() 

Decorators are used in frameworks like Flask and Django for authentication and logging. 

19. What are iterators and generators? 

• Iterator: An object implementing __iter__() and __next__(). 
• Generator: A function that yields values using yield. 

Example: 

my_list = [1, 2, 3] 
iterator = iter(my_list) 
print(next(iterator)) 

Generators save memory as they don’t store all data at once. 

20. What are commonly used built-in functions in Python? 

Function	Purpose
len()	Returns length
type()	Returns data type
sum()	Sums all elements
max() / min()	Finds largest/smallest
sorted()	Returns sorted list
range()	Generates number sequence
dir()	Lists available attributes
help()	Displays documentation

These detailed Python interview questions and answers for freshers will strengthen your foundation and prepare you to handle both conceptual and coding-based interview rounds. Understanding why and how each concept works is more valuable than memorizing syntax.nn

Python Coding Interview Questions and Answers 

Preparing for a Python coding interview requires more than just knowing syntax. You need to understand logic, problem-solving, and how to apply concepts in real-world coding tasks. Below are some of the most common Python coding interview questions with simple explanations and code examples to help you get ready. 

1. Reverse a String in Python 

Question: How do you reverse a string in Python? 

Answer: 

You can reverse a string in several ways. The simplest is using slicing. 

text = "Python" 
reversed_text = text[::-1] 
print(reversed_text) 

Output: 

nohtyP 

Explanation: 

The slice [::-1] tells Python to start from the end and move backward. 

How to start the answer: 

 Begin by explaining that strings in Python are immutable and then show the most efficient solution using slicing. 

2. Find the Factorial of a Number 

Question: Write a Python program to find the factorial of a number. 

Answer: 

You can solve this using either recursion or loops. 

def factorial(n): 
    if n == 0: 
        return 1 
    else: 
        return n * factorial(n - 1) 
print(factorial(5)) 

Output: 

120 

Explanation: 

The recursive function multiplies each number until it reaches 1. 

How to start the answer: 

Explain what a factorial means (n! = n × (n-1) × ... × 1), then introduce recursion as one of the methods to implement it. 

3. Check if a Number is Prime 

Question: How do you check if a number is prime in Python? 

Answer: 

A prime number is greater than 1 and divisible only by 1 and itself. 

def is_prime(num): 
    if num <= 1: 
        return False 
    for i in range(2, int(num ** 0.5) + 1): 
        if num % i == 0: 
            return False 
    return True 
print(is_prime(11)) 

Output: 

True 

Explanation: 

We only check divisibility up to the square root of the number for efficiency. 

How to start the answer: 

Start by defining what a prime number is, then outline the logic before writing the code. 

Also Read: Prime Number in Python: 7 Easy Methods with Code and Explanation 

4. Find the Largest Element in a List 

Question: How can you find the largest element in a Python list? 

Answer: 

You can use the built-in max() function or write your own loop. 

numbers = [3, 8, 12, 5, 9] 
largest = max(numbers) 
print(largest) 

Output: 

12 

Explanation: 

The max() function returns the highest value in the list. 

How to start the answer: 

Mention that Python provides built-in functions like max() but also explain how to do it manually for clarity. 

5. Count the Frequency of Each Element in a List 

Question: How do you count the occurrences of each element in a list? 

Answer: 

You can use a dictionary or Python’s collections.Counter. 

from collections import Counter 
 
items = ['apple', 'banana', 'apple', 'orange', 'banana'] 
count = Counter(items) 
print(count) 

Output: 

Counter({'apple': 2, 'banana': 2, 'orange': 1}) 

Explanation: 

Counter() automatically counts how many times each element appears. 

How to start the answer: 

Begin by saying you can solve this either with loops or by using built-in modules like collections. 

6. Check for Palindrome 

Question: How do you check if a string is a palindrome? 

Answer: 

A palindrome reads the same backward and forward. 

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

Output: 

True 

Explanation: 

Converting to lowercase and removing spaces ensures accurate comparison. 

How to start the answer: 

Start with the definition of a palindrome and show a clean, practical code approach. 

Also Read: How To Check Palindrome Number in Python? 

7. Remove Duplicates from a List 

Question: How can you remove duplicates from a list in Python? 

Answer: 

You can use a set() or list comprehension. 

numbers = [1, 2, 2, 3, 4, 4, 5] 
unique = list(set(numbers)) 
print(unique) 

Output: 

[1, 2, 3, 4, 5] 

Explanation: 

set() automatically removes duplicates because sets only store unique elements. 

How to start the answer: 

Explain that Python sets are ideal for handling duplicates and demonstrate conversion back to a list. 

8. Swap Two Variables Without a Temporary Variable 

Question: How do you swap two variables in Python without using a third variable? 

Answer: 

Python allows tuple unpacking. 

a = 10 
b = 20 
a, b = b, a 
print(a, b) 

Output: 

20 10 

Explanation: 

Tuple unpacking swaps values directly, making it clean and readable. 

How to start the answer: 

Mention that traditional swapping uses a temporary variable but Python simplifies it using tuple assignment. 

9. Find the Second Largest Number in a List 

Question: Write a Python program to find the second largest number in a list. 

Answer: 

You can sort the list or use logic with max(). 

numbers = [4, 1, 8, 7, 5] 
numbers.sort() 
print(numbers[-2]) 

Output: 

7 

Explanation: 

Sorting rearranges the list, and [-2] accesses the second largest element. 

How to start the answer: 

Explain that sorting is one of the easiest methods, then show an example. 

10. Check if Two Strings are Anagrams 

Question: How do you check if two strings are anagrams? 

Answer: 

 Two strings are anagrams if they contain the same characters in a different order. 

def is_anagram(s1, s2): 
    return sorted(s1.lower()) == sorted(s2.lower()) 
 
print(is_anagram("listen", "silent")) 

Output: 

True 

Explanation: 

Sorting both strings and comparing them is an easy way to check for anagrams. 

How to start the answer: 

Define what an anagram means, then explain how sorting or counting characters helps verify it. 

Also Read: Anagram Program in Python 

Quick Reference Table 

Concept	Approach	Key Function/Method
Reverse a string	Slicing	[::-1]
Factorial	Recursion or loop	def factorial()
Prime check	Loop up to √n	for i in range(2, int(n**0.5)+1)
Largest element	Built-in function	max()
Count elements	Using Counter	collections.Counter()
Palindrome	String comparison	s == s[::-1]
Remove duplicates	Convert to set	set()
Swap variables	Tuple unpacking	a, b = b, a
Second largest	Sorting	list.sort()
Anagram check	Sorting strings	sorted()

Python Coding Interview Questions and Answers (Advanced Level) 

Here are 10 more Python coding interview questions that test logic, data handling, and algorithmic thinking. Each includes clear explanations, code samples, and how to begin your answer confidently in an interview. 

1. Find the Fibonacci Series up to N Terms 

Question: Write a Python program to print the Fibonacci sequence up to n terms. 

Answer: 

 Fibonacci series is a sequence where each number is the sum of the two preceding ones. 

def fibonacci(n): 
    a, b = 0, 1 
    for _ in range(n): 
        print(a, end=" ") 
        a, b = b, a + b 
 
fibonacci(8) 

Output: 

0 1 1 2 3 5 8 13 

Explanation: 

The loop updates a and b with each iteration to generate the next term. 

How to start the answer: 

Say that Fibonacci numbers follow a simple recurrence relation and mention the use of iteration for efficiency. 

2. Find the Missing Number in a List 

Question: Given a list of consecutive numbers with one missing, find the missing number. 

Answer: 

You can use the formula for the sum of n natural numbers. 

def find_missing(nums): 
    n = len(nums) + 1 
    total = n * (n + 1) // 2 
    return total - sum(nums) 
 
print(find_missing([1, 2, 3, 5, 6])) 

Output: 

4 

Explanation: 

The difference between the expected total and actual total gives the missing number. 

How to start the answer: 

Explain that you’ll use a mathematical formula to avoid looping through every element. 

3. Find All Even Numbers in a List 

Question: How do you extract all even numbers from a list? 

Answer: 

Use list comprehension for a concise solution. 

numbers = [10, 15, 20, 25, 30] 
evens = [n for n in numbers if n % 2 == 0] 
print(evens) 

Output: 

[10, 20, 30] 

Explanation: 

The condition n % 2 == 0 filters only even numbers. 

How to start the answer: 

Mention that list comprehension is a Pythonic way to filter data. 

Also Read: Python Cheat Sheet: From Fundamentals to Advanced Concepts for 2025 

4. Merge Two Dictionaries 

Question: How can you merge two dictionaries in Python? 

Answer: 

From Python 3.9 onwards, you can use the | operator. 

dict1 = {'a': 1, 'b': 2} 
dict2 = {'c': 3, 'd': 4} 
merged = dict1 | dict2 
print(merged) 

Output: 

{'a': 1, 'b': 2, 'c': 3, 'd': 4} 

Explanation: 

The | operator merges two dictionaries without overwriting either. 

How to start the answer: 

Mention the older .update() method first, then highlight the modern | operator as a cleaner approach. 

5. Find the Sum of Digits of a Number 

Question: Write a Python program to find the sum of digits of a number. 

Answer: 

You can convert the number to a string and loop through it. 

num = 1234 
sum_digits = sum(int(digit) for digit in str(num)) 
print(sum_digits) 

Output: 

10 

Explanation: 

Each digit is converted to an integer and added using sum(). 

How to start the answer: 

Explain that strings make digit iteration simple, then show the clean one-line approach. 

6. Check if Two Lists are Identical 

Question: How do you check if two lists contain the same elements? 

Answer: 

You can compare their sorted versions. 

list1 = [1, 2, 3] 
list2 = [3, 2, 1] 
print(sorted(list1) == sorted(list2)) 

Output: 

True 

Explanation: 

 Sorting ensures both lists are in the same order for comparison. 

How to start the answer: 

Start by explaining that direct equality checks require the same order, hence sorting helps. 

7. Find Common Elements Between Two Lists 

Question: How do you find common elements between two lists in Python? 

Answer: 

Convert both lists into sets and use intersection. 

a = [1, 2, 3, 4] 
b = [3, 4, 5, 6] 
common = list(set(a) & set(b)) 
print(common) 

Output: 

[3, 4] 

Explanation: 

The & operator returns the intersection of both sets. 

How to start the answer: 

Say that sets make this task easier as they handle unique elements and allow direct intersection. 

8. Sort a Dictionary by Its Values 

Question: How do you sort a dictionary by its values in Python? 

Answer: 

Use the sorted() function with a lambda key. 

data = {'apple': 3, 'banana': 1, 'cherry': 2} 
sorted_data = dict(sorted(data.items(), key=lambda x: x[1])) 
print(sorted_data) 

Output: 

{'banana': 1, 'cherry': 2, 'apple': 3} 

Explanation: 

The lambda function sorts based on the second element (value) of each pair. 

How to start the answer: 

Begin by saying that dictionaries are unordered by default, so you use sorted() for control. 

Also Read: Sort Dictionary by Value in Python 

9. Find the Length of a String Without Using len() 

Question: How can you find the length of a string without using len()? 

Answer: 

Loop through the string and count characters manually. 

def string_length(s): 
    count = 0 
    for _ in s: 
        count += 1 
    return count 
 
print(string_length("Python")) 
  

Output: 

6 

Explanation: 

Each loop iteration increases the count by one. 

How to start the answer: 

Explain that you’re simulating how len() works internally by counting elements one by one. 

10. Find the Intersection of Two Arrays Without Using Set 

Question: Write a program to find common elements between two lists without using sets. 

Answer: 

You can use list comprehension. 

a = [1, 2, 3, 4] 
b = [3, 4, 5, 6] 
intersection = [i for i in a if i in b] 
print(intersection) 

Output: 

[3, 4] 

Explanation: 

The list comprehension checks each element in a against b. 

How to start the answer: 

Say that this approach works well when you want to maintain list order and avoid using additional data structures. 

Quick Summary Table 

Problem	Concept Used	Key Function/Method
Fibonacci sequence	Loop iteration	for loop
Missing number	Arithmetic formula	sum()
Even numbers	List comprehension	[n for n in list]
Merge dicts	Merge operator	`
Sum of digits	String conversion	sum()
Compare lists	Sorting	sorted()
Common elements	Set intersection	&
Sort dict	Lambda function	sorted()
String length	Loop counting	for loop
Array intersection	List comprehension	if i in b

Python Interview Questions and Answers for Experienced 

These Python interview questions and answers for experienced professionals focus on topics like OOPs, decorators, generators, multithreading, and performance optimization. Each question includes a clear explanation, sample code, and tips on how to start your answer during interviews. 

1. What are Python decorators and why are they used? 

Answer: 

A decorator lets you modify or extend the behavior of a function or class method without changing its actual code. 

Example: 

def decorator_func(func): 
    def wrapper(): 
        print("Before the function runs") 
        func() 
        print("After the function runs") 
    return wrapper 
 
@decorator_func 
def greet(): 
    print("Hello!") 
 
greet() 

Output: 

Before the function runs   
Hello!   
After the function runs   

Explanation: 

Decorators are often used for logging, authentication, or performance checks. 

How to start the answer: 

Start by saying: “Decorators allow code reusability by wrapping functions dynamically, helping manage cross-cutting concerns like logging or access control.” 

2. Explain Python generators and their benefits. 

Answer: 

Generators are functions that yield one value at a time using the yield keyword instead of returning all results at once. 

Example: 

def counter(): 
    for i in range(5): 
        yield i 
 
for num in counter(): 
    print(num) 

Output: 

0 1 2 3 4 

Explanation: 

They save memory and improve efficiency for large datasets since they don’t store all values in memory. 

How to start the answer: 

Say: “Generators are memory-efficient iterators useful when dealing with large sequences or streaming data.” 

3. What is the difference between shallow copy and deep copy in Python? 

Answer: 

• Shallow Copy: Copies references of objects (changes reflect in both). 
• Deep Copy: Creates a new independent copy. 

Example: 

import copy 
list1 = [[1, 2], [3, 4]] 
shallow = copy.copy(list1) 
deep = copy.deepcopy(list1) 
 
list1[0][0] = 99 
print(shallow) 
print(deep) 

Output: 

[[99, 2], [3, 4]]   
[[1, 2], [3, 4]] 

How to start the answer: 

Begin with: “A shallow copy only duplicates references, while a deep copy duplicates actual objects, ensuring independence.” 

4. How does Python manage memory? 

Answer: 

Python uses automatic garbage collection and reference counting to manage memory. 

Key points: 

• Every object has a reference count. 
• When count drops to zero, the object is deleted. 
• The gc module handles cyclic references. 

Example: 

import gc 
gc.collect() 

How to start the answer: 

Say: “Python handles memory automatically using reference counting and garbage collection, reducing manual effort for developers.” 

Also Read: The Efficient Memory Management in Python Guide 

5. What are Python closures? 

Answer: 

A closure is a function defined inside another function that remembers the outer function’s variables even after the outer function has finished executing. 

Example: 

def outer(x): 
    def inner(y): 
        return x + y 
    return inner 
 
add_5 = outer(5) 
print(add_5(10)) 

Output: 

15 

Explanation: 

Closures help retain state between function calls. 

How to start the answer: 

Start by saying: “Closures allow inner functions to capture and use values from their enclosing scope even after execution.” 

6. What is multithreading in Python and why is it limited by the GIL? 

Answer: 

Multithreading allows multiple threads to run concurrently. However, Python’s Global Interpreter Lock (GIL) permits only one thread to execute Python bytecode at a time. 

Example: 

import threading 
 
def task(): 
    print("Running task") 
 
t1 = threading.Thread(target=task) 
t2 = threading.Thread(target=task) 
t1.start() 
t2.start() 

Explanation: 

GIL prevents parallel execution in CPU-bound tasks but works well for I/O-bound tasks. 

How to start the answer: 

Say: “Multithreading in Python improves I/O-bound task performance, though the GIL restricts true parallel CPU execution.” 

7. What are context managers in Python? 

Answer: 

Context managers handle resource management automatically using with statements. 

Example: 

with open("file.txt", "r") as file: 
    data = file.read() 

Explanation: 

They automatically close files or connections after use, even if an error occurs. 

How to start the answer: 

Say: “Context managers simplify resource cleanup, commonly used for files, database connections, or network sessions.” 

8. Explain the difference between @staticmethod, @classmethod, and instance methods. 

Answer: 

Method Type	Access	Uses
Instance Method	Accesses instance variables	Most common
Class Method	Uses cls instead of self	For class-level data
Static Method	No self or cls	For utility functions

Example: 

class Demo: 
    def instance_method(self): 
        print("Instance Method") 
    @classmethod 
    def class_method(cls): 
        print("Class Method") 
    @staticmethod 
    def static_method(): 
        print("Static Method") 

How to start the answer: 

 Say: “Instance methods work on object data, class methods on shared class data, and static methods are independent utility functions.” 

Also Read: Sum of N Natural Numbers 

9. What are Python metaclasses? 

Answer: 

A metaclass defines how classes behave. In Python, everything (even a class) is an object, and metaclasses control their creation. 

Example: 

class Meta(type): 
    def __new__(cls, name, bases, dct): 
        print("Creating class:", name) 
        return super().__new__(cls, name, bases, dct) 
 
class Test(metaclass=Meta): 
    pass 

Output: 

Creating class: Test 

How to start the answer: 

Say: “Metaclasses define the behavior of classes themselves, acting as templates for class creation.” 

10. How can you optimize Python code performance? 

Answer: 

 Common optimization techniques: 

• Use built-in functions like map() and sum(). 
• Use NumPy for numerical operations. 
• Apply generators for large datasets. 
• Use Cython or PyPy for faster execution. 
• Avoid unnecessary loops and global variables. 

How to start the answer: 

Say: “Optimization involves improving execution speed through efficient data handling, reduced memory usage, and leveraging built-in libraries.” 

Quick Reference Table 

Topic	Key Concept	Example Use
Decorators	Function wrapping	Logging, validation
Generators	Lazy evaluation	Streaming data
Copying	Object duplication	Shallow vs Deep
Memory Management	Garbage Collection	gc.collect()
Closures	State retention	Function factories
Multithreading	Concurrent tasks	I/O operations
Context Managers	Resource handling	File operations
Class Methods	Class-level logic	Factory patterns
Metaclasses	Class creation logic	Frameworks
Optimization	Performance tuning	NumPy, Cython

Advanced Python Interview Questions and Answers for Experienced Professionals 

This extended section covers advanced Python interview questions and answers for experienced developers focusing on frameworks, async I/O, design patterns, and real-world performance challenges. Each question includes clear code examples, explanations, and a short guide on how to start your answer effectively during an interview. 

1. What is asynchronous programming in Python? 

Answer: 

 Asynchronous programming allows tasks to run independently, improving performance for I/O-bound operations such as network calls or file access. Python achieves this using asyncio, async, and await keywords. 

Example: 

import asyncio 
 
async def fetch_data(): 
    print("Fetching data...") 
    await asyncio.sleep(2) 
    print("Data fetched") 
 
asyncio.run(fetch_data()) 

Output: 

Fetching data... 
Data fetched 

Explanation: 

Instead of blocking execution, await pauses only the current coroutine while others continue running. 

How to start the answer: 

Say: “Asynchronous programming in Python enables concurrency for I/O tasks using coroutines with async and await.” 

Also Read: JavaScript Async Await 

2. How does Python handle exceptions efficiently? 

Answer: 

Exception handling prevents program crashes and allows safe error recovery using try, except, else, and finally. 

Example: 

try: 
    result = 10 / 0 
except ZeroDivisionError: 
    print("Cannot divide by zero") 
else: 
    print("Division successful") 
finally: 
    print("Process completed") 
  

Output: 

Cannot divide by zero   
Process completed 

Explanation: 

• try: contains risky code. 
• except: handles errors. 
• else: executes if no error occurs. 
• finally: runs regardless of outcome. 

How to start the answer: 

 Say: “Python uses structured exception handling blocks that isolate faulty code, making applications more reliable.” 

3. What is the difference between deep and lazy imports in Python? 

Answer: 

• Deep import: Loads all dependencies immediately when the module is imported. 
• Lazy import: Loads modules only when used. 

Example: 

import importlib 
math = importlib.import_module("math") 
print(math.sqrt(16)) 

Explanation: 

Lazy imports improve startup time and reduce memory usage for large applications. 

How to start the answer: 

Say: “Lazy imports delay module loading until required, optimizing performance in large-scale applications.” 

4. What is monkey patching in Python? 

Answer: 

Monkey patching modifies or extends code at runtime without altering the source code. 

Example: 

class Test: 
    def greet(self): 
        print("Hello") 
 
def new_greet(): 
    print("Hi there!") 
 
Test.greet = new_greet 
t = Test() 
t.greet() 

Output: 

Hi there! 

Explanation: 

Used in testing or debugging, but it can make code harder to maintain. 

How to start the answer: 

Say: “Monkey patching dynamically changes class or module behavior, often used in testing or hotfixes.” 

5. Explain Python’s Global Interpreter Lock (GIL) in detail. 

Answer: 

The GIL ensures only one thread executes Python bytecode at a time, even on multi-core systems. 

Implications: 

• CPU-bound tasks don’t benefit from threads. 
• I/O-bound tasks (network or file operations) do benefit. 

Alternatives: 

• Use multiprocessing for true parallelism. 
• Use libraries like NumPy or Cython for performance gains. 

How to start the answer: 

Say: “The GIL in Python limits one thread to run at a time to manage memory safely, but it restricts CPU-bound parallelism.” 

Also Read: Python NumPy Tutorial: Learn Python Numpy With Examples 

6. What are design patterns in Python? Give examples. 

Answer: 

Design patterns are reusable solutions for common software problems. 

Common patterns: 

• Singleton: Ensures one instance of a class. 
• Factory: Creates objects without specifying exact classes. 
• Observer: Notifies dependent objects of state changes. 

Example (Singleton): 

class Singleton: 
    _instance = None 
    def __new__(cls): 
        if not cls._instance: 
            cls._instance = super().__new__(cls) 
        return cls._instance 

How to start the answer: 

 Say: “Design patterns provide time-tested templates for building scalable and maintainable Python applications.” 

7. What is the difference between multiprocessing and multithreading? 

Answer: 

Feature	Multithreading	Multiprocessing
Execution	Same process, multiple threads	Multiple processes
GIL Impact	Affected by GIL	Not affected
Use Case	I/O-bound tasks	CPU-bound tasks

Example: 

from multiprocessing import Process 
def task(): 
    print("Running task") 
 
p1 = Process(target=task) 
p1.start() 
p1.join() 

How to start the answer: 

 Say: “Multithreading improves I/O-bound operations, while multiprocessing enables parallel execution for CPU-intensive work.” 

8. How can you handle large files efficiently in Python? 

Answer: 

 Use generators, file streaming, or chunk processing to avoid loading everything into memory. 

Example: 

def read_large_file(file_path): 
    with open(file_path, 'r') as file: 
        for line in file: 
            yield line.strip() 

Explanation: 

Reading line by line avoids memory overflow with large files. 

How to start the answer: 

Say: “For large files, generators and streaming allow memory-efficient reading without overloading the system.” 

9. How do you handle memory leaks in Python? 

Answer: 

 Memory leaks occur when objects remain referenced unintentionally. 

Ways to prevent: 

• Use the gc module to track objects. 
• Avoid circular references. 
• Use weak references for temporary objects. 

Example: 

import gc 
gc.collect() 

How to start the answer: 

 Say: “Memory leaks in Python can be avoided by managing references carefully and using garbage collection tools effectively.” 

Also Read: How to Open a File in Python? A Beginner’s Guide with Examples 

10. How does async I/O differ from multithreading? 

Answer: 

Feature	Async I/O	Multithreading
Execution	Single thread, cooperative multitasking	Multiple threads, preemptive
Best for	I/O-bound tasks	Mixed tasks
Libraries	asyncio	threading

Example: 

import asyncio 
 
async def download(): 
    print("Downloading...") 
    await asyncio.sleep(1) 
    print("Done") 
 
asyncio.run(download()) 

How to start the answer: 

 Say: “Async I/O runs concurrent tasks within one thread, while multithreading uses separate threads that share memory.” 

Quick Reference Table 

Concept	Use Case	Key Module
Async I/O	Network calls	asyncio
Exception Handling	Code reliability	try-except
Lazy Imports	Performance	importlib
Monkey Patching	Testing	—
GIL	Thread control	—
Design Patterns	Code structure	abc, custom classes
Multiprocessing	Parallel execution	multiprocessing
File Handling	Large data	Generators
Memory Leaks	Resource cleanup	gc, weakref
Async vs Threads	Task concurrency	asyncio, threading

Python Interview Scenario-Based Questions 

Scenario-based questions test how you apply Python concepts in real-world situations. They help interviewers gauge your problem-solving ability, logical thinking, and code clarity. Below are common Python interview questions and answers you may face, along with tips on how to answer them confidently. 

1. You have a list of numbers. How would you find all duplicate elements? 

Answer: 

You can use a set to track duplicates easily. 

nums = [1, 2, 3, 4, 2, 3, 5] 
duplicates = list(set([x for x in nums if nums.count(x) > 1])) 
print(duplicates) 

Output: 

[2, 3] 

How to answer: 

Start by explaining that using set helps remove duplicates. Mention that this method works well for small lists but is not optimal for very large datasets since count() increases time complexity. 

2. How would you handle missing values in a dataset using Python? 

Answer: 

Using pandas: 

import pandas as pd 
 
data = {'A': [1, 2, None, 4], 'B': [5, None, 7, 8]} 
df = pd.DataFrame(data) 
 
# Fill missing values with mean 
df['A'].fillna(df['A'].mean(), inplace=True) 
df['B'].fillna(df['B'].median(), inplace=True) 

How to answer: 

Say that you identify missing values with isnull() or info(). Then explain that you can fill or drop them depending on data importance. Show knowledge of using fillna(), dropna(), and handling strategy selection (mean, median, mode). 

Also Read: Understanding Python Data Types 

3. How would you optimize a slow Python script? 

Answer: 

 Common methods include: 

• Using list comprehensions instead of loops. 
• Avoiding global variables. 
• Using built-in functions like sum(), map(), filter(). 
• Applying multiprocessing for parallel tasks. 
• Profiling code using cProfile. 

import cProfile 
cProfile.run('my_function()') 

How to answer: 

 Explain that you’d first identify bottlenecks using profiling tools. Then mention replacing inefficient code with optimized structures or libraries (like NumPy for vectorized operations). 

4. How would you merge two dictionaries in Python 3.9+? 

Answer: 

You can use the merge (|) operator. 

dict1 = {'a': 1, 'b': 2} 
dict2 = {'b': 3, 'c': 4} 
 
merged = dict1 | dict2 
print(merged) 

Output: 

{'a': 1, 'b': 3, 'c': 4} 

How to answer: 

Mention that this operator was introduced in Python 3.9. It’s cleaner than using update() and returns a new dictionary instead of modifying the existing one. 

5. You have to read a large file (5GB). How would you handle it efficiently? 

Answer: 

You can use generators or file streaming. 

def read_large_file(file_name): 
    with open(file_name) as f: 
        for line in f: 
            yield line.strip() 

How to answer: 

Explain that reading the file line by line avoids loading the entire file into memory. It’s efficient and scalable. Mention the difference between using readlines() and generators. 

6. How would you remove duplicates from a list while keeping the order? 

Answer: 

Use a simple loop with a set to track seen items. 

def remove_duplicates(seq): 
    seen = set() 
    result = [] 
    for item in seq: 
        if item not in seen: 
            result.append(item) 
            seen.add(item) 
    return result 

How to answer: 

Explain that sets are unordered but useful for uniqueness checks. This method preserves the list’s original order while removing duplicates efficiently. 

7. How would you handle exceptions in a large Python project? 

Answer: 

Use try-except blocks and custom exception classes. 

class CustomError(Exception): 
    pass 
 
try: 
    result = 10 / 0 
except ZeroDivisionError: 
    print("Cannot divide by zero.") 
except CustomError as e: 
    print(e) 

How to answer: 

Explain that exception handling ensures stability. Use finally for cleanup and custom exceptions for better debugging and code readability. 

Also Read: Top 50 Python Project Ideas with Source Code in 2025 

8. How would you connect Python with a database? 

Answer: 

You can use SQLite, MySQL, or SQLAlchemy. 

import sqlite3 
 
conn = sqlite3.connect('example.db') 
cursor = conn.cursor() 
cursor.execute("CREATE TABLE users (id INTEGER, name TEXT)") 
conn.commit() 
conn.close() 

How to answer: 

Say that you’d establish a connection, create a cursor, execute SQL commands, and close the connection. For large projects, mention using ORM tools like SQLAlchemy for abstraction. 

9. How would you write unit tests for a Python function? 

Answer: 

Using the unittest module. 

import unittest 
 
def add(a, b): 
    return a + b 
 
class TestMath(unittest.TestCase): 
    def test_add(self): 
        self.assertEqual(add(2, 3), 5) 
 
if __name__ == '__main__': 
    unittest.main() 

How to answer: 

Explain the importance of testing individual components. Mention frameworks like pytest for more readable and concise tests. 

10. How would you design a Python function that retries an API call on failure? 

Answer: 

Use retry logic with loops and exception handling. 

import time 
import requests 
 
def fetch_data(url, retries=3): 
    for attempt in range(retries): 
        try: 
            response = requests.get(url) 
            if response.status_code == 200: 
                return response.json() 
        except requests.exceptions.RequestException: 
            time.sleep(2) 
    return None 

How to answer: 

Explain that retries prevent program failure during network errors. You can also use libraries like tenacity for structured retry handling. 

How to Start Answers in Scenario-Based Questions 

When answering, begin by: 

1. Restating the problem briefly to show understanding. 
2. Explaining your thought process before writing code. 
3. Presenting clean, readable code with comments if needed. 
4. Describing your reasoning for choosing that approach. 

Example starter lines: 

• “I’d start by analyzing the data structure…” 
• “To handle this efficiently, I’d use…” 
• “The optimal solution here involves using…” 

These Python interview scenario-based questions reflect real-world challenges you’ll likely face as a developer. Focus on clarity, logic, and scalability when answering. 

Also Read: Step-by-Step Guide to Learning Python for Data Science 

Tips to Crack Python Interviews in 2025 

Preparing for a Python interview in 2025 means going beyond syntax. You need a mix of coding ability, problem-solving skills, and practical experience. Here’s how you can stand out. 

1. Strengthen Core Python Concepts 

• Revise data types, loops, functions, OOP, and exception handling. 
• Understand iterators, generators, and decorators. 
• Practice questions on lists, dictionaries, and sets. 

Tip: Interviewers often test how well you can use Python’s built-in functions instead of writing extra code. 

2. Practice Coding Problems Regularly 

• Solve coding challenges on platforms like LeetCode, HackerRank, or CodeSignal. 
• Focus on string manipulation, file handling, and algorithm-based problems. 
• Simulate interview conditions by timing yourself. 

Tip: Learn to explain your approach out loud as you solve — communication counts. 

3. Master Popular Libraries 

• Learn NumPy, Pandas, Matplotlib, and Requests. 
• For advanced roles, add Flask, Django, and SQLAlchemy. 
• Understand how to apply these in data analysis, automation, or web development tasks. 

Tip: Be ready to discuss real examples of projects you’ve built using these libraries. 

4. Understand Time and Space Complexity 

• Practice writing code with O(n) or better efficiency. 
• Use profiling tools like cProfile to check performance. 

Tip: Always explain why your approach is efficient and how you’d optimize it further. 

5. Be Ready for Scenario-Based and Debugging Questions 

• Expect practical tasks like reading large files, handling APIs, or writing reusable code. 
• Practice debugging errors and improving existing code snippets. 

Tip: Talk through your logic clearly, even if you’re unsure — interviewers value reasoning more than memorization. 

These Python interview tips for 2025 will help you prepare smartly and present your skills with confidence. The key is to combine theory, coding practice, and real-world application to stand out in every round. 

Conclusion 

This blog includes common python interview questions and answers, covering important topics like data types, functions, object-oriented programming, and coding challenges.  

However, preparing for such interviews can feel overwhelming, especially when you encounter tricky questions, including basic python interview questions, that require clear, practical responses. 

To help bridge this gap, upGrad’s personalized career guidance can help you explore the right learning path based on your goals. You can also visit your nearest upGrad center and start hands-on training today! 

References:

1. https://www.liquidweb.com/blog/latest-python-version/