Python OOPS Interview question

Object-Oriented Programming (OOP) is a fundamental approach used to build scalable, maintainable, and readable Python applications. Despite Python's flexibility and dynamic nature, OOP in Python is considered a key tool for application architecture, API development, data modeling, and organizing business logic. That’s why Python interviews almost always include OOP-related questions — both for junior and senior-level roles.

Interviewers assess not just syntax knowledge: they want to see how well you understand why classes are used, how to implement encapsulation, inheritance, and polymorphism, how you design systems, and make architectural decisions. It’s important to be able to explain your reasoning: why you used @property, how composition is structured, and when you prefer an interface over an abstract class. Knowledge of magic methods, correct use of super(), and adherence to SRP and SOLID principles — all reflect a developer’s maturity.

Essential Python OOPs Interview Questions and Answers

In this section, you’ll find basic and most common questions on object-oriented programming in Python. These questions test your understanding of key concepts: what a class and object are, how encapsulation works, what self means, the role of __init__, and how inheritance is structured. Good answers show that you haven’t just read about OOP — you actually use it in projects. It’s the foundation upon which clean architecture is built. Solid grasp of these concepts is the minimum requirement for any Python developer.

1. What is a class and an object in Python, and how would you explain them to a beginner?

I usually explain it this way: a class is a blueprint, and an object is a specific instance created from that blueprint. The class defines what properties (variables) and behaviors (methods) an object will have. For example, if we have a Car class, each car would have a brand, color, and a drive() method. An object is an actual car, like a red BMW. In Python, classes are created with the class keyword, and objects are instantiated by calling the class like a function. It’s a core concept upon which everything else in OOP is built.

2. What is encapsulation and how is it implemented in Python?

Encapsulation is a way to hide an object’s internal state and control access to it through methods. Python doesn’t enforce strict privacy, but there are conventions. For example, naming an attribute with a single underscore _balance signals “don’t touch this directly.” With double underscores __balance, name mangling is applied, making the variable harder to access externally. I use encapsulation to control how values are modified — via @property and @setter. This protects the object from invalid changes and makes it easy to add validation later. Python offers flexibility, but good practice is to hide internal details rather than relying on user discipline.

3. Explain inheritance in Python. When do you use it?

Inheritance allows one class (child) to take everything from another (parent) and optionally override or extend its behavior. I use it when I see multiple classes share common attributes or methods. For example, I might have a User class, and then AdminUser and ClientUser inherit from it. Shared logic stays in User, while specific logic is defined in the subclasses. Python supports both single and multiple inheritance, but I’m careful with the latter — it can get confusing. I always use super() to properly call parent methods, especially during initialization. Inheritance is powerful, but I apply it only when it simplifies the architecture, not complicates it.

4. What is polymorphism and how does it work in Python?

Polymorphism is the ability to interact with different objects through the same interface. Python makes it easy because it’s dynamically typed. For instance, if I have several classes each with a draw() method implemented differently, I can loop through a list of these objects and call obj.draw() — Python calls the correct implementation. That’s classic polymorphism. I use it when I want code that’s agnostic of specific implementations. It’s especially useful in collections where you work with different but compatible objects. The key is ensuring each has the right method signature.

5. How does the __init__ method work, and what should or shouldn't you do in it?

__init__ is the object constructor. It’s called immediately after an instance is created and is used to initialize its attributes. You can set object properties, accept parameters, and even call other methods. But some things shouldn’t be done in __init__ — like running code that could fail or put a heavy load on the system. I try to keep __init__ lightweight: initialize fields and set initial state. For complex object creation, I use separate class methods like from_config() or from_json(). That keeps __init__ clean and the code easy to read.

6. What is self and why is it needed in class methods?

self is a reference to the current object. It allows you to access attributes and other methods of that specific instance. In Python, it’s passed as the first argument to every method in a class. Without it, Python wouldn’t know which object the method is acting on. For example, self.name refers to the name field of the current instance. It’s not a keyword — just a convention, but it’s mandatory. Without it, methods won’t work properly. It’s important to understand that self appears only inside the class — when a method is called externally, Python passes it automatically.

7. What is the difference between a class and an instance of a class?

A class is like a blueprint or design — it defines what fields and methods an object will have. An instance is a specific object created from that class. For example, if you have a class Cat, then cat1 = Cat() is an instance. The class exists once, but you can create many objects from it. Each instance can have different field values, but they share the same methods from the class. I always separate the two in my mind: when writing logic — I think about the class; when handling data — I deal with instances.

8. What is method overloading in Python and how is it implemented?

In classical OOP, overloading means having multiple methods with the same name but different parameters. Python doesn’t support this natively — it doesn’t allow method overloading by signature. Instead, I handle it inside one method using parameter checks. For example, if a method accepts either a string or a list — I use isinstance() to check and act accordingly. Another approach is using default arguments or *args and **kwargs. It’s not overloading in the traditional sense, but it gives flexibility to handle multiple cases in a single function.

9. What are magic methods in Python and why are they useful?

Magic methods are special methods with double underscores at the beginning and end, like __init__, __str__, __len__, __eq__, etc. They allow classes to interact with Python’s built-in behavior. For example, __str__ defines how the object prints with print(), __eq__ defines object comparison, and __add__ defines addition. I use them when I want my object to behave like a native Python object — support operations, comparisons, nice printing, etc. The key is not to overuse them. If a class becomes too “magical,” it gets hard to understand. But properly implemented magic methods make your code clearer and more user-friendly.

10. What is composition, and when do you choose it over inheritance?

Composition is an approach where one class includes another as an attribute, rather than inheriting from it. I use it when I want to reuse functionality while keeping classes independent. For example, if I have a Logger and want to add logging to a DataProcessor, I just create self.logger = Logger() inside. This is better than inheriting from Logger because logging logic and data processing logic are separate concerns. Composition provides flexibility: you can change behavior without altering the inheritance structure. Inheritance is for hierarchies, composition is for flexible relationships. Python supports both equally well, and I choose the one that simplifies the architecture.

11. What is an abstract class in Python and why is it useful?

An abstract class is a class that cannot be instantiated directly and serves as a base for other classes. In Python, I use the abc module, inherit from ABC, and mark methods with the @abstractmethod decorator. It’s useful when I want to define a structure — for example, when all child classes must implement a process() method, but I don't yet know how it will be implemented. An abstract class can contain shared behavior and enforce required methods. It helps on the architectural level: other developers can see which methods are required, avoiding forgotten implementations. It's especially helpful in large projects and team environments.

12. What is an interface in Python and how does it differ from an abstract class?

Python doesn’t have interfaces in the classical sense like Java, but you can simulate them using abstract classes without method implementations. I create a base class with @abstractmethod and define only method signatures. The difference is that an interface is a contract: it should not contain any implementation, only methods that subclasses must implement. In Python, I can also use Protocol from typing to define an interface without inheritance — especially useful with mypy. In general, an interface is a way to say: “this object can do this,” without specifying how.

13. What is MRO (Method Resolution Order) and how does it work in Python?

MRO is the order in which Python looks for methods when using multiple inheritance. It’s based on the C3 Linearization algorithm. If a class inherits from several others, Python builds a search chain: it first looks in the class itself, then the first parent, then the second, and so on, following specific rules. I’ve run into this when using super(): if you don’t understand MRO, you might accidentally call the wrong method. You can check the order using Class.__mro__ or Class.mro(). The key is to avoid diamond inheritance unless necessary. MRO is powerful but requires careful handling.

14. What does the super() function do, and why is it important to use it correctly?

super() returns an object that gives access to a parent class. I use it to call parent methods without hardcoding the parent’s name. This is especially important with multiple inheritance: super() respects MRO and calls the correct method next in the chain. It makes the architecture flexible. For example, if I have Child(A, B) and both A and B call super(), everything will be invoked in the correct order. But if I call a parent directly, I can break the chain. So I always use super(), even if there’s only one parent for now — for future-proofing.

15. How does @property work, and why is it useful?

@property lets you turn a method into an attribute. It’s useful when you want to hide logic behind a “simple” access pattern. For example, instead of obj.get_area() I can write obj.area. It reads better, and the logic can still be calculated inside. Combined with @<prop>.setter, I can control assignment: add validation, calculation, or restrict changes. It’s a form of encapsulation: the external interface stays simple while the internal logic remains protected. I use it when I want to provide a clean interface while keeping tight control over the data.

16. What is a descriptor in Python and when would you use one?

A descriptor is a class that manages access to an attribute in another class. It implements __get__, __set__, and __delete__. I’ve used descriptors when I needed, for example, to log every access to a field or validate a value on assignment. They offer more flexibility than @property, especially when you want reusable behavior across multiple classes. One descriptor can be attached to different fields — and it works consistently. They are useful in libraries, ORMs, and frameworks. In regular application development, descriptors are rare, but when you need full control — they’re a powerful tool.

17. Explain the difference between __str__ and __repr__ — when do you use each?

__str__ defines how an object is displayed when printed — it should be readable and user-friendly. I usually return something like return f'User({self.name})'. __repr__ is what Python shows in interactive mode and logs. It should be as precise as possible and ideally recreate the object. If __str__ is not defined, __repr__ is used as fallback. I always implement both: __str__ for readability, __repr__ for debugging. It’s a small thing, but interviewers love to ask about it — and they’re right.

18. Can you explain dynamic attribute creation and when it can be dangerous?

In Python, you can create attributes on the fly: obj.new_attr = 123. That’s powerful, but can lead to bugs. For example, a typo can create two different fields when there should be one. Or someone might assign an attribute the method didn’t expect. To restrict this, I use __slots__ — it prevents creation of new attributes beyond those defined. It also saves memory. I allow dynamic attributes when creating objects from JSON or API responses, but I always validate the structure. In general, flexibility is great — but only if you control it.

19. How do you implement polymorphism in real projects?

Usually via a common interface or abstract base class. For example, I might have several data loaders: CSVLoader, JSONLoader, SQLLoader, all with a load() method. In code, I just call loader.load() — and it works regardless of the format. This makes the code extensible: want a new source? Just create a new class with load() and register it. Python makes polymorphism easy: no need to declare types — just follow the structure. It’s especially handy when writing code that works with different objects but expects consistent behavior.

20. How do you design classes, and which OOP principles do you follow in Python?

I always start by asking: “Do I really need a class?” If there’s state and behavior that logically go together — it’s a class candidate. When designing, I follow SRP (Single Responsibility Principle): one class — one responsibility. I follow the open/closed principle: classes should be open for extension but closed for modification — easily achieved with inheritance or composition. In Python, readability is key: I avoid overengineering. Less magic, more clarity. And of course, I write code that’s testable and extensible — the class should make sense not just to me, but to whoever maintains it later.