50+ OOPs in Python Interview Questions and Answers with Pro Tips

Python's popularity is growing rapidly, especially with OOPs Concepts. The demand for Python professionals with OOPs skills is high, making it a valuable asset.

This article covers all types of OOPs in Python interview questions to help you crack the Python interviews and build a strong foundation in software development.

Best 50+ OOPs in Python Interview Questions and Answers: 

Whether you're a fresher or an experienced developer, understanding OOP in Python is crucial for writing scalable and maintainable code. This handpicked set of Python OOP interview questions will help you grasp key concepts like inheritance, encapsulation, polymorphism, method overloading, and design patterns—all explained with practical clarity.

At this point, if you are searching for Python-related interview questions, then here's the 100th set of Python Interview Q&As for freshers to help you prepare for interviews.

Q1. What is Object-Oriented Programming in Python?

Object-Oriented Programming (OOP) is a programming paradigm that uses objects which has its own attributes, functions, and classes to structure code. It enables the four main principles such as encapsulation, inheritance, and polymorphism to build modular and reusable applications.

Q2. What is a class in Python?

A class in python is a blueprint for creating objects. It defines properties and behaviour of attributes and methods that the objects created from the class.

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

Q3. What is an object in Python?

An object is an instance of a class. It encapsulates data and behavior defined by the class. Objects represents the real world entities or abstract concepts that can be manipulated and interacted with in a program. 

my_dog = Dog("Buddy")

Q4. How can Object-Oriented Programming improve the design of background task processing using Celery in Python?

OOPs helps organise Celery tasks into reusable and modular structures. Instead of writing standalone functions, tasks can be encapsulated within classes, promoting better separation of concerns and easier testing. This is especially useful in large Django projects.

from celery import shared_task

class EmailTasks:
    @staticmethod
    @shared_task
    def send_welcome_email(user_id):
        # Logic to send email
        print(f"Sending email to user {user_id}")

Here's what to do next, click on this Python Celery Tutorial with Django - Async Task Queues for understanding how Celery integrate into Django using OOP-friendly patterns.

Q5. What is the difference between a class and an object?

The key difference between a class and an object is that a class defines the structure and behaviour while an object is a specific instance with its own attributes and functions. A class is instantiated to create an object, whereas multiple objects can be created from a single class.

Q6. What are instance variables?

Instance variables are the attributes of a class that are unique to each instance. Instance variables are defined in __init__ using self. Each object has its own copy.

Q7. What are class variables?

Class variables are shared across all instances. They are defined directly within the class, outside any methods, and typically store  information that are common to all instances.

class Dog:
    species = "Canis familiaris"  # class variable

    def __init__(self, name):
       self.name = name  # instance variable
dog1 = Dog("Buddy")
dog2 = Dog("Max")
print(dog1.species)  # Output: Canis familiaris
print(dog2.species)  # Output: Canis familiaris
Dog.species = "Canis lupus familiaris"
print(dog1.species)  # Output: Canis lupus familiaris

Also check this out on Python 3.14 version release for applying real-life coding examples in better way.

Q8. What are instance methods?

Instance methods are functions that are defined inside a class and are bound to an instance of the class. They operate on an object’s attributes using self.

Q9. What is a class method?

A class method is a method that takes cls as the first parameter and operates on the class rather than the instance. A class method have full access to its class variables. It can also be used to create constructor.

@classmethod
def from_year(cls, year):
    return cls(year)

Q10. What is a static method?

A static method is a method that is defined using the @staticmethod  decorator and does not take self or cls.It rather acts like a regular function inside the class.

@staticmethod
def utility():
    return "Static method"

Q11. What is inheritance in Python?

Inheritance in Python is a fundamental concepts in OOPs that have the process of  one class to inherit the attributes and methods of another. The inheriting is called subclass or derived class while the class being inherited from is called superclass or based class. 

Q12. How do you implement inheritance in Python?

Inheritance in Python is basically implemented by specifying the superclass para thesis after the subclass name. Here's example:

class Animal:
    def speak(self):
        print("Animal speaks")

class Dog(Animal):
    pass

Q13. What is multiple inheritance?

Multiple Inheritance is a feature of OOPs that allows a subclass to inherit properties and behavior from more than one superclass. This means that a subclass can inherit attributes and methods from multiple parent classes.

class A: pass
class B: pass
class C(A, B): pass

Q14. How do design principles enhance Object-Oriented Programming in Python/Django projects?

Design principles like SOLID, DRY, and separation of concerns strengthen OOP by making your Django apps more scalable and maintainable. These principles encourage writing clean, modular classes and reusable methods, which is essential for long-term project health.

From this point, if you want to dive deeper, then go forward with Design principles in Python/Django. 

Q15. What is MRO in Python?

MRO which stands for Multiple Resolution Order, determines the order in which base classes are searched when executing a method. It follows C3 linearization to compute MRO.

Q16. What is encapsulation?

Encapsulation is a fundamental concept in OOPs that hides the internal state of objects and allows access via public methods. This helps to hide the internal implementation details and expose only the necessary information to the outside world.

Q17. What is polymorphism in Python?

Polymorphism in Python is a core concept in OOPs that allows different classes to define methods with the same name that behave differently. There are three types of polymorphism: method overriding, method overloading, and function polymorphism.

Q18. What is method overloading in Python?

Method overloading is a feature in Python that allows multiple methods with the same name to be defined, but with different parameter lists. However, Python does not support traditional method overloading. So you have to use default arguments instead.

Q19. What is method overriding?

Method overriding in Python allows a child class to provide a specific implementation of a method that is already defined in its parent class. That means redefining a method in a child class already defined in the parent class. The overridden method in the child class replaces the parent class method when called through the child class instance.

Q20. What is an abstract class in Python?

An abstract class in Python is a class that cannot be instantiated on its own and is designed to be inherited by other classes. It provides a template for other classes to implement common interfaces or functionality, especially when certain methods should be mandatory for all child classes to define.  You can use the abc module to define. Here's example:

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass

class Circle(Shape):
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return 3.14 * self.radius ** 2

# shape = Shape()  # Raises TypeError
circle = Circle(5)
print(circle.area())  # Output: 78.5

This example shows the Shape class as an abstract class that defines an abstract area method. The Circle class inherits from Shape and provides an implementation for the area method.

Q21. What is an interface in Python?

An interface is an abstract class that defines a contract or a set of methods that must be implemented by any class that inherits from it. Python doesn’t have interfaces like Java, but abstract base classes can be used similarly.

Q22. What is composition in Python OOP?

Composition in Python OOPs is an essential concept that allows you to create complex objects from simpler ones. An object that is the container owns or manages a collection of other objects or values in composition using one class within another instead of inheritance.  It promotes code reuse by allowing you to create complex objects from simpler ones. Keep in mind that It represents a "has-a" relationship rather than an "is-a" relationship. For example:

class Engine:
    def start(self):
        print("The engine is starting.")

class Car:
    def __init__(self):
        self.engine = Engine()

    def start_car(self):
        self.engine.start()

my_car = Car()
my_car.start_car()  # Output: The engine is starting.

This example shows that the Car class is the container object that owns an Engine object. The Engine object is the contained object that provides the start method.

Q23. What is the difference between composition and inheritance?

The key difference between composition and inheritance is that composition represents a "has-a" relationship, whereas  Inheritance represents an "is-a" relationship. Composition promotes loose coupling, while Inheritance can lead to tight coupling.

Q24. What is the use of super()?

 The super() function in Python is used to give access to methods and properties of a parent or sibling class. It allows access to the parent class’s methods or constructors without explicitly naming the parent class, which makes your code more maintainable and supports multiple inheritance. For example:

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

    def speak(self):
        return f"{self.name} makes a sound"

class Dog(Animal):
    def __init__(self, name, breed):
        super().__init__(name)  # Call parent constructor
        self.breed = breed

    def speak(self):
        base_sound = super().speak()  # Call parent method
        return f"{base_sound}, and barks like a {self.breed}"

dog = Dog("Buddy", "Labrador")
print(dog.speak())

Output

Buddy makes a sound, and barks like a Labrador

Make sure to always use super() with care in multiple inheritance, and understand the MRO to avoid unexpected behavior.

Q25. What are private variables in Python?

private variables are variables that are intended to be private to a class and should not be accessed directly from outside the class. Python does not enforce strict access restrictions, but it uses name mangling to discourage access to private variables. Private variables are defined by prefixing with __. 

Q26. What are protected members?

Protected members are variables or methods that are intended to be accessed within a class and its subclasses, but not directly from outside the class. They are denoted by being prefixed with _. 

Q27. Can you create your own exception classes?

Yes, you can create your own exception classes in Python by deriving from the built-in Exception class or one of its subclasses. Custom exception classes allow you to provide more specific and meaningful error messages, making it easier to handle and debug exceptions in your code. For Example:

class InsufficientFundsError(Exception):
    def __init__(self, balance, amount):
        self.message = f"Cannot withdraw {amount}. Current balance is {balance}."
        super().__init__(self.message)

# Usage
class BankAccount:
    def __init__(self, balance):
        self.balance = balance

    def withdraw(self, amount):
        if amount > self.balance:
            raise InsufficientFundsError(self.balance, amount)
        self.balance -= amount
        return self.balance

# Testing
try:
    account = BankAccount(100)
    account.withdraw(150)
except InsufficientFundsError as e:
    print(f"Error: {e}")

Q28. What is a property in Python?

A property is a way to implement getter, setter, and deleter functionality for an attribute of a class. It is a way to manage attribute access using @property decorator. Here's an example using @property:

class Temperature:
    def __init__(self, celsius):
        self._celsius = celsius

    @property
    def celsius(self):
        return self._celsius

    @celsius.setter
    def celsius(self, value):
        if value < -273.15:
            raise ValueError("Temperature cannot go below absolute zero.")
        self._celsius = value

    @celsius.deleter
    def celsius(self):
        del self._celsius

# Usage
temp = Temperature(25)
print(temp.celsius)      # calls getter
temp.celsius = 30        # calls setter
del temp.celsius         # calls deleter

Q29. What is a descriptor in Python?

A descriptor in Python is a protocol that allows an object to implement attribute access for another object. They are used to implement properties, methods, and other types of attributes that require custom access logic.  An object attribute with “binding behaviour,” is defined by implementing __get__, __set__, and __delete__.

Q30. What is operator overloading?

Operator overloading is a feature in Python that allows you to redefine the behaviour of operators such as +, -, *, /, ==, !=, >, < , etc. for custom classes. This is done by implementing special methods (also called magic methods or dunder methods) in your class. For example:

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __add__(self, other):
        return Point(self.x + other.x, self.y + other.y)

    def __str__(self):
        return f"({self.x}, {self.y})"

p1 = Point(1, 2)
p2 = Point(3, 4)
print(p1 + p2)  # Output: (4, 6)

In this example, the + operator is overloaded using the __add__ method to add two Point objects.

Q31. What is duck typing in Python?

Duck typing is a concept in Python where the type or class of an object is less important than the methods or behaviours it defines. This approach is based on the idea that "if it walks like a duck and quacks like a duck, then it's a duck."If an object behaves like a duck (implements needed methods), it's treated as such, regardless of its actual class.

Q32. What is the Global Interpreter Lock (GIL)?

The Global Interpreter Lock (GIL) is a mutex mechanism used in CPython, the standard implementation of the Python programming language, that allows only one thread to execute Python bytecode at a time, affecting multithreaded performance.

Q33. How does Python handle memory management for objects?

Python's memory management is handled by a private heap, which is a pool of memory allocated by the operating system for Python's exclusive use. Python manages memory automatically using reference counting and cyclic garbage collection.

Q34. What is a metaclass in Python?

A metaclass is a class that creates classes. Metaclasses are used to define the behaviour of classes, such as how they are initialised, how their attributes are accessed, and how they are instantiated.  Here's a by default example that uses the built-in type as the metaclass for all classes.

class MyClass:
    pass
print(type(MyClass))  # Output: <class 'type'>

Q35. Can you explain object slicing?

Object slicing refers to the process where an object of a derived class is assigned to a base class object, causing the derived-specific attributes to be "sliced off" (lost). Python does not suffer from object slicing in the same way because of its dynamic nature and how it handles objects and inheritance.

Q36. How are objects compared in Python?

Objects can be compared using various operators, including equality operators and comparison operators. In equality operators, the == operator checks if the values of two objects are equal, and the is operator checks if two objects are the same instance., whereas Classes in comparison operators can define custom comparison behavior by implementing special methods like __lt__, __gt__, __le__, and __ge__.  

Q37. What are mixins, and when would you use them in Python?

Mixins are classes that provide a set of methods that can be used by other classes without being their base class. They are a form of multiple inheritance, where a class can inherit behavior from multiple mixin classes.

Q38. What is the use of __slots__?

__slots__ is a special attribute in Python classes that allows you to explicitly declare the data attributes (instance variables) of a class. You can improve memory efficiency, prevent dynamic attribute creation by using _slots_. Here's an Syntax example : 

class Person:
    __slots__ = ['name', 'age']  # only allow these attributes

    def __init__(self, name, age):
        self.name = name
        self.age = age

p = Person("Alice", 30)
print(p.name)  # Alice

# Trying to assign an attribute not listed in __slots__ raises an error
p.city = "New York"  # AttributeError: 'Person' object has no attribute 'city'

Q39. What is the difference between is and ==?

is and == are two distinct operators used for comparing objects in python. The is operator checks if two objects are the same instance whereas The == operator checks if the values of two objects are equal. So use the == operator when comparing values and is operator when checking if two variables refer to the same object instance.

Q40. What is the singleton pattern in Python?

The singleton pattern is a design pattern that restricts a class from instantiating multiple objects. It ensures that only one instance of the class exists, and provides a global point of access to that instance.

class Singleton:
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance

# Usage
obj1 = Singleton()
obj2 = Singleton()

print(obj1 is obj2)  # True → both refer to the same instance

Q41. Define a base class Animal with subclasses Dog and Cat, each implementing a make_sound() method.

class Animal:
    def make_sound(self):
        raise NotImplementedError("Subclasses must implement this method")

class Dog(Animal):
    def make_sound(self):
        return "Woof!"

class Cat(Animal):
    def make_sound(self):
        return "Meow!"

# Usage
a = Dog()
b = Cat()
print(a.make_sound())  # Woof!
print(b.make_sound())  # Meow!

Q42. Write a class hierarchy for Account, SavingsAccount, and CurrentAccount, where each subclass overrides a method get_interest_rate().

class Account:
    def get_interest_rate(self):
        return 0

class SavingsAccount(Account):
    def get_interest_rate(self):
        return 4.0  # 4% interest

class CurrentAccount(Account):
    def get_interest_rate(self):
        return 0.5  # 0.5% interest

# Usage
sa = SavingsAccount()
ca = CurrentAccount()
print(sa.get_interest_rate())  # 4.0
print(ca.get_interest_rate())  # 0.5

Q43. Design a Shape base class with Rectangle and Circle as subclasses. Each subclass should implement an area() method.

import math

class Shape:
    def area(self):
        raise NotImplementedError("Subclass must implement abstract method")

class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

class Circle(Shape):
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return math.pi * self.radius ** 2

# Usage
r = Rectangle(4, 5)
c = Circle(3)
print(r.area())  # 20
print(c.area())  # 28.27...

Q44.How do Django models and forms demonstrate Object-Oriented Programming in Python?

Django models and forms are Python classes that encapsulate data and behavior — a core OOP principle. Models define database structure via class attributes, while forms handle input validation and logic. From here, also learn Django models and forums through real examples on how OOP shapes Django development.

Q45. Can a class implement multiple design patterns?

Yes, a class can implement multiple design patterns simultaneously. A single class may combine elements from different design patterns to solve complex problems efficiently in practice. You can create more robust, flexible, and maintainable software systems by combining multiple designs.

Q46. What is the Adapter pattern in Python?

The Adapter pattern in Python is a structural design pattern that allows incompatible interfaces to work together. It acts as a bridge between two incompatible classes by converting the interface of one class into an interface expected by the clients. This way, objects with incompatible interfaces can collaborate without modifying their existing code.

Q47. What is the difference between @classmethod and @staticmethod?

@classmethod and @staticmethod are two decorators  in Python used to define methods that belong to a class rather than an instance of the class.@classmethod receives the class as the first argument (cls), while @staticmethod does not receive any implicit arguments. Also  @classmethod has access to the class attributes and can modify class state, while @staticmethod does not.

Q48.What is meant by Garbage Collection in OOPs World? 

Garbage Collection (GC) in the OOP (Object-Oriented Programming) world refers to the automatic process of identifying and reclaiming memory occupied by objects that are no longer in use or accessible by a program. This helps prevent memory leaks and reduces the risk of bugs caused by dangling pointers. For example:

import gc

class Node:
    def __init__(self):
        self.ref = None

a = Node()
b = Node()
a.ref = b
b.ref = a  # Creates a cycle

del a
del b

gc.collect()  # Forces garbage collection

Q49. How is OOP supported in Python modules and packages?

 Python supports OOP in modules (single .py files) and packages (directories with multiple modules). You can define and reuse classes across modules, use inheritance between classes in different files, and organise code logically. This enables scalable, modular, and maintainable object-oriented programs.

Q50. How does Python achieve data hiding?

Python uses naming conventions for data hiding. Prefixing an attribute with a single underscore (e.g., _value) signals it’s intended for internal use. Double underscores (e.g., __value) trigger name mangling, making the attribute harder to access from outside the class (_ClassName__value). This discourages direct access, promoting encapsulation, though it doesn't enforce strict privacy.

Q51. How do you make an object iterable?

To make an object iterable, you must define the __iter__() method that returns an iterator. The iterator must implement a __next__() method that returns values one at a time and raises StopIteration when done.

class Counter:
    def __init__(self, limit):
        self.limit = limit
        self.current = 0

    def __iter__(self):
        return self

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

# Usage
for num in Counter(3):
    print(num)  # Output: 0, 1, 2

This example shows that it makes Counter an iterable object using the iterator protocol.

Q52. What’s the difference between composition and aggregation?

The Key difference between composition and aggregation is that the container owns the contained object in composition, whereas the container only references the contained object in aggregation. The lifetime of the contained object is tied to the container in composition, while the contained object can exist independently in aggregation.

Q53. How do you clone an object in Python?

 You can clone an object using the copy module, which provides two functions: copy() and deepcopy() in Python. You can easily create copies of objects in Python by using the copy module, and avoid unintended modifications to the original object.

import copy

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

p1 = Person("Alice", ["Python", "Django"])
p2 = copy.deepcopy(p1)
p2.skills.append("Flask")

print(p1.skills)  # ['Python', 'Django']
print(p2.skills)  # ['Python', 'Django', 'Flask']

Pro Tips: to Crack Python OOPs Interview

If you want to crack a Python OOPs interview with ease, then: 

• Master the Fundamentals of OOPs 
• Practice coding questions and DSA
• Prepare for behavioural questions
• Ask questions for clarification

Now also, look at best practices for structuring Django if you want to crack Django Interview questions!

Conclusion 

These OOPs questions cover the core principles, common patterns, and advanced techniques used in Python development. You'll not only impress interviewers but also write more efficient, readable, and reusable Python code.

If you spot any mistakes or feel any answer could be improved, feel free to drop a comment and let me know. I appreciate your feedback to make this resource even better!