Python is one of the few programming languages that still dominates the entire web development industry. Every seasoned web developer knows the worth and power of this language. And what makes it even more compelling is its versatility in using both object-oriented programming (OOP) and functional paradigms. By functional paradigms, we understood that it is related to the web’s functionalities but what is OOPs in Python? Let’s have a look at this key concept through this guide. 

In Python, OOPs (Object-oriented Programming) is a paradigm that utilizes classes and objects in programming to implement real-world entities, such as polymorphisms, abstraction, inheritance, and encapsulation. 

No matter whether you are a web developer, software developer, or machine learning engineer, having a clear and basic understanding of object-oriented programming concepts is a must-have skill to get started in the world of web development. 

In this comprehensive guide to understanding the OOPs in Python, we will explain everything about this key programming paradigm and its concepts with examples. So, let’s get started! 

What Is OOPs in Python?

Object-oriented programming (OOP) in Python is a programming paradigm based on the concept of "objects", which include both data and code. The data is stored as properties (also called attributes), and the code is defined as methods (functions that the object can execute).

To design a program using an object-oriented paradigm, we use classes and objects. Python programming language allows us to use different programming methods like functional programming, and modular programming. 

Python OOPs is one of the common methods to solve a programming problem by making objects.

An object has these two features:

• Attribute
• Behavior

A key aspect of OOP in Python is to make code that can be used again by using the concept of inheritance. This concept is also known as DRY (Don't Repeat Yourself).

What are Class and Object in Python?

Class and Object are the two of the most important concepts in Python. But what are they and how can they be used in OOPs? Let’s take a look at these concepts deeply. 

Python Class 

A class defines the structure and behavior of the objects that belong to it. It is a conceptual entity that has some properties and functions. To illustrate the purpose of creating a class, let’s take an example. 

Imagine you wanted to keep track of the number of cats that have various properties like breed and age. If you used a list, you would have to store the cat’s breed in the first position and its age in the second position. But what if you had 100 different cats? How would you remember which position corresponds to which property? And what if you wanted to add more features to these cats? This is not a good way to organize the data and that’s why we need classes. 

Some key points on Python Class:  

• Classes are defined by the keyword class.
• Attributes are the data members that are associated with a class.
• Attributes can be accessed with the dot (.) operator. For instance: Myclass.Myattribute

Python Object

A Python object can be understood as an entity that has both state and behavior. It can be anything from the real world, such as a mouse, keyboard, chair, table, pen, etc. Objects also include numbers, strings, lists, and dictionaries. 

For example, the number 10 is an object, the string “Hello, world” is an object, a list is an object that can contain other objects, etc. You have been working with objects all this time without knowing it.

An object has three components:

• State: It is the set of attributes that an object has. It also shows the characteristics of an object.
• Behavior: It is the set of methods that an object can perform. It also shows how an object reacts to other objects.
• Identity: It is the unique name that an object has. It allows one object to communicate with other objects.

To illustrate the state, behavior, and identity, let us use the example of the class Cats (explained above). 

• The identity is the name of the Cat.
• The state or attributes are the breed, age, or color of the Cat.
• The behavior is whether the Cat is eating or sleeping.

Understanding OOPs Concepts in Python with Examples

In this section, we will explain the four pillars of OOPs in Python. Understanding these concepts will help you succeed in the web development world. So, take a look at them with an example. 

Concept I: Inheritance

Inheritance is the ability of one class to acquire or inherit the attributes from another class. The class that acquires attributes is known as the child class or derived class and the class from which the attributes are acquired is known as the parent class or base class. 

The advantages of inheritance are:

• It models real-world relationships well.
• It enables the code to be reused. We do not have to repeat the same code over and over. Also, it lets us extend a class without changing it.
• It follows the transitive property, which means that if class B inherits from another class A, then all the subclasses of B would automatically inherit from class A.

Types of Inheritance

• Single Inheritance: A derived class can acquire the features of a single-parent class through single-level inheritance.
• Multilevel Inheritance: A derived class can obtain the attributes of an immediate parent class, which itself inherits the attributes of its own parent class, through multi-level inheritance.
• Hierarchical Inheritance: More than one derived class can share the same features of a parent class through hierarchical-level inheritance.
• Multiple Inheritance: A derived class can combine the features of more than one base class through multiple-level inheritance.

Example of Inheritance

Here is an example of inheritance in Python, which is a popular object-oriented programming language. Python uses a colon (:) and indentation to indicate the subclass and superclass relationship.

Python

# Define a superclass called Animal

class Animal:

  # Define a constructor method that initializes the name attribute

  def __init__(self, name):

    self.name = name

  # Define a method that prints the sound of the animal

  def make_sound(self):

    print(self.name + " makes a sound")

# Define a subclass called Dog that inherits from Animal

class Dog(Animal):

  # Define a method that prints the breed of the dog

  def show_breed(self, breed):

    print(self.name + " is a " + breed)

# Create an instance of Animal

animal = Animal("Leo")

# Call the make_sound method inherited from Animal

animal.make_sound()

# Output: Leo makes a sound

# Create an instance of Dog

dog = Dog("Max")

# Call the make_sound method inherited from Animal

dog.make_sound()

# Output: Max makes a sound

# Call the show_breed method defined in Dog

dog.show_breed("Labrador")

# Output: Max is a Labrador

Concept II: Polymorphism

Polymorphism is the ability to take on different shapes. For instance, if we want to check whether a certain kind of bird can fly or not, we can use polymorphism to achieve this with one function.

This code illustrates the idea of inheritance and method overriding in Python classes. It shows how subclasses can modify methods that are declared in their parent class to suit their own needs while still using other methods from the parent class.

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Example of Polymorphism

Here is an example of polymorphism with a function and objects in Python:

Python

# Define a function that can take any object as an argument

def describe(obj):

    # Call the name and info methods of the object

    print(obj.name())

    print(obj.info())

# Define a class called Animal

class Animal:

    # Define a method called name

    def name(self):

        return "Animal"

    # Define a method called info

    def info(self):

        return "An animal is a living creature."

# Define a class called Dog that inherits from Animal

class Dog(Animal):

    # Override the name method

    def name(self):

        return "Dog"

    # Override the info method

    def info(self):

        return "A dog is a domestic animal that can bark."

# Define a class called Cat that inherits from Animal

class Cat(Animal):

    # Override the name method

    def name(self):

        return "Cat"

    # Override the info method

    def info(self):

        return "A cat is a domestic animal that can meow."

# Create an object of Animal class

animal = Animal()

# Create an object of Dog class

dog = Dog()

# Create an object of Cat class

cat = Cat()

# Call the describe function with different objects

describe(animal)

describe(dog)

describe(cat)

The output of this code is:

An animal is a living creature.

A dog is a domestic animal that can bark.

A cat is a domestic animal that can meow.

Concept III: Encapsulation

Encapsulation is one of the basic principles of object-oriented programming (OOP) in Python. It means that data and the functions that operate on data are grouped together in one unit. This limits the direct access to variables and functions and protects the data from being altered unintentionally. 

To ensure data integrity, an object's variable can only be modified by an object's function. Such variables are called private variables. A class illustrates the concept of encapsulation as it contains all the data that is member functions, variables, etc.

Example of Encapsulation

Here is an example of encapsulation with a class and private members in Python:

Python

# Define a class called BankAccount

class BankAccount:

    # Define a constructor that takes an initial balance as an argument

    def __init__(self, balance):

        # Define a private instance variable called _balance

        # Private variables start with an underscore and cannot be accessed directly

        self._balance = balance

    # Define a public method called deposit that takes an amount as an argument

    def deposit(self, amount):

        # Add the amount to the private _balance variable

        self._balance += amount

        # Print a message

        print(f"You deposited {amount} and your new balance is {self._balance}")

    # Define a public method called withdraw that takes an amount as an argument

    def withdraw(self, amount):

        # Check if the amount is less than or equal to the private _balance variable

        if amount <= self._balance:

            # Subtract the amount from the private _balance variable

            self._balance -= amount

            # Print a message

            print(f"You withdrew {amount} and your new balance is {self._balance}")

        else:

            # Print an error message

            print(f"You cannot withdraw more than your balance of {self._balance}")

    # Define a public method called get_balance that returns the private _balance variable

    def get_balance(self):

        return self._balance

# Create an object of BankAccount class with an initial balance of 1000

account = BankAccount(1000)

# Call the deposit method with 500 as an argument

account.deposit(500)

# Call the withdraw method with 200 as an argument

account.withdraw(200)

# Call the get_balance method and print the result

print(f"Your final balance is {account.get_balance()}")

The output of this code is:

You deposited 500 and your new balance is 1500

You withdrew 200 and your new balance is 1300

Your final balance is 1300

As you can see, the BankAccount class encapsulates the data and methods related to a bank account. The _balance variable is private and can only be modified or accessed by the methods of the class. This prevents unauthorized or accidental changes to the data. The deposit, withdraw, and get_balance methods are public and can be used by anyone who has an object of the class. 

Concept IV: Data Abstraction

Data Abstraction allows developers to hide details of how the function works internally from the end users. It only shows the users a simple way to interact with each object. A method that is only defined by its name and not by its actions is an abstract method. 

An abstract method has an empty body. An abstract class is a class that contains an abstract method. Python does not have abstract classes by default, but there is a module called ABC that enables Python to create abstract methods and classes.

Example of Data Abstraction

Here is a simple example of data abstraction in Python using abstract classes:

Python

# Import the abstract base class module

from abc import ABC, abstractmethod

# Define an abstract class called Animal

class Animal(ABC):

    # Define an abstract method called make_sound

    @abstractmethod

    def make_sound(self):

        # Empty body

        pass

# Define a subclass called Dog that inherits from Animal

class Dog(Animal):

    # Implement the make_sound method

    def make_sound(self):

        # Print "Woof"

        print("Woof")

# Define a subclass called Cat that inherits from Animal

class Cat(Animal):

    # Implement the make_sound method

    def make_sound(self):

        # Print "Meow"

        print("Meow")

# Create an instance of Dog

dog = Dog()

# Call the make_sound method

dog.make_sound()

# Create an instance of Cat

cat = Cat()

# Call the make_sound method

cat.make_sound()

The output of this code is:

Woof

Meow

As you can see, we have achieved data abstraction by using abstract classes. The user of the Animal class does not need to know how each subclass implements the make_sound method, they only need to know that every subclass of Animal has this method and can use it. This way, we can hide the implementation details and only expose the essential functionalities.

Is Mastering OOPs in Python a Good Career Decision?

Python OOP is a valuable skill that can boost your career prospects. You can work as a back-end developer, data scientist, machine learning engineer, and more. 

Additionally, the demand for such job profiles is expected to grow by 15% from 2020 to 2030, according to the Bureau of Labor Statistics, which is much higher than the average growth rate for all jobs. 

What about the salary? Payscale reports that the median salary for a Python developer in the United States is about $79,395 per year as of 2023. But if you have a solid grasp of Python OOP and some experience, you can make much more than that.

So, learning Python OOP is not just a way to improve your skills but also to secure your future career.

20+ OOPs in Python Interview Questions

If you have mastered OOP concepts and planning to get a job as an expert Python developer, here’s a list of the top 20 Python OOPs interview questions that will help you prepare for your dream job. So, let’s take a look at them:

1. What are the benefits of using OOP in Python?
2. How do you define a class and create an object in Python?
3. What are the differences between class variables and instance variables?
4. How do you implement inheritance and polymorphism in Python?
5. What are abstract classes and interfaces in Python? How do you use them?
6. What are the built-in methods that every Python class has? How can you override them?
7. What are the differences between `__str__` and `__repr__` methods in Python?
8. How do you implement multiple inheritance in Python? What are the advantages and disadvantages of using it?
9. What is the diamond problem in multiple inheritance? How does Python resolve it?
10. What are the differences between `is` and `==` operators in Python?
11. What are the differences between shallow copy and deep copy in Python? How do you create them?
12. What are decorators in Python? How do you use them to enhance the functionality of a class or a method?
13. What are the differences between static methods and class methods in Python? How do you define and invoke them?
14. What are the differences between public, private, and protected attributes in Python? How do you enforce them?
15. What are the differences between `__new__` and `__init__` methods in Python? How do you use them to customize the object creation process?
16. What are the differences between composition and aggregation in Python? How do you implement them?
17. What are the differences between `*args` and `**kwargs` in Python? How do you use them to define and call a method with a variable number of arguments?
18. What are the differences between `super()` and `self` in Python? How do you use them to access the attributes and methods of the parent class?
19. What are the differences between `@property` and `@setter` decorators in Python? How do you use them to create and modify the properties of a class?
20. What are the differences between `__getattr__` and `__getattribute__` methods in Python? How do you use them to customize the attribute access of a class?

Conclusion

In the above tutorial, we put our best efforts into explaining what OOPs in Python and its key concepts, including Class, Object, Encapsulation, Abstraction, Inheritance, and Polymorphism. With the help of our comprehensive examples, you can understand the topic more precisely. 

With this, we conclude the topic but remember it's not the end, there is much more to explore in OOP Python. This article has given you a solid foundation of OOP concepts. Now you can confidently read any other article on this topic, and you will not feel lost or confused.

FAQs

What is the OOPs Concept?

Python OOPs Concept is a way of programming that organizes data and behavior into reusable units called objects.

What are the 4 Pillars of OOP Python?

The 4 Pillars of OOP Python are encapsulation, abstraction, inheritance, and polymorphism.

What are the Methods in OOP Python?

The most common methods in OOPs concepts in Python are functions that belong to objects and can access or modify their attributes.

What are the 4 Basics of OOP?

The 4 Basics of OOP are class, object, attribute, and method.

Why do We Need OOP?

We Need OOP because it makes code more modular, maintainable, reusable, and extensible.

What is a Real-life Example of OOPs?

A Real-life Example of OOPs is a car, which is an object that has attributes like color, model, speed, etc., and methods like start, stop, accelerate, etc.