Skip to main content

Method Overriding in Python

 


Learning Sections          show

Method Overriding in Python

Method overriding allows a subclass to provide a specific implementation for a method that is already defined in its superclass. This is particularly useful when the behavior needs to be extended or modified. Overriding ensures that the method in the subclass has the same name, return type, and parameters as the method in the parent class.

When a method in a subclass has the same signature as a method in its superclass, the subclass method overrides the superclass method. This allows the subclass to provide a specific implementation of the method.

Let's explore an example to understand method overriding in detail:

Example: Method Overriding

class Animal:
    # Method in the superclass
    def speak(self):
        return "Animal speaks"

class Dog(Animal):
    # Overriding the method in the subclass
    def speak(self):
        return "Dog barks"

animal = Animal()
dog = Dog()

print(animal.speak())  # Output: Animal speaks
print(dog.speak())    # Output: Dog barks
Explanation:

In the above example, we have a superclass Animal with a method speak that returns the string "Animal speaks". The subclass Dog inherits from Animal and overrides the speak method to return "Dog barks".

When we create an instance of Animal and call the speak method, it returns "Animal speaks". However, when we create an instance of Dog and call the speak method, it returns "Dog barks". This demonstrates how the speak method in the Dog class overrides the speak method in the Animal class.

Using super() to Call the Superclass Method

In some cases, we might want to call the overridden method from the superclass in the subclass. This can be done using the super() function. Here's an example:


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

class Dog(Animal):
    def speak(self):
        # Calling the superclass method using super()
        parent_speak = super().speak()
        return parent_speak + " and Dog barks"

dog = Dog()
print(dog.speak())  # Output: Animal speaks and Dog barks

In this example, the speak method in the Dog class calls the speak method from the Animal class using super() and then appends additional behavior to it.

Labels

Labels:

Popular posts from this blog

Learn Python

  Learning Sections Introduction to Python Comment, escape sequence and print statement in Python Variables and Data Types in Python Typecasting in Python User input in Python String slicing and operations on string in Python String methods in Python If else conditional statements in Python Match case statement in Python For loops in Python While loops in Python Break and continue statement in Python Functions in Python Function Arguments in Python introduction to lists in Python List methods in Python Tuples in Python Operations on tuple in Python f strings in Python Docstrings in Python Recursion in Python Sets in Python Set methods in Python Dictionaries in Python for Loop with else in Python Exception Handling in Python Finally keyword in Python Raising custom errors in Python Short hand if else statements Enumerate Function in Python Virtual Environment in Python How import works in Python if __nam...
Read more

MultiProcessing in Python

  Learning Sections          show MultiProcessing in Python Multiprocessing in Python involves using the multiprocessing module to run multiple processes concurrently, taking advantage of multiple CPU cores. This module provides a higher level of concurrency than threading and is especially useful for CPU-bound tasks. Creating Processes You can create and start a new process by using the multiprocessing module: import multiprocessing def print_numbers (): for i in range ( 10 ): print ( i ) p1 = multiprocessing.Process ( target = print_numbers ) p1 . start () p1 . join () # Wait for the process to complete Using Process Pools The multiprocessing module provides a Pool class, which allows you to manage a pool of worker processes: from multiprocessing import Pool def square ( n ): return n * n with Pool ( 4 ) as pool : result = pool.map ( square , range (...
Read more

Conclusion and where to go after this

  Conclusion and Where to Go After This Congratulations on completing your Python learning journey! You've covered a wide array of topics, from the basics of syntax and data types to advanced concepts like multithreading, multiprocessing, and decorators. But learning doesn't stop here. Python is a versatile language with many specialized fields where you can apply your skills. Here are some potential paths you can explore next: Machine Learning Machine Learning (ML) is one of the most exciting fields you can dive into. Python's libraries like TensorFlow, Keras, scikit-learn, and PyTorch make it an ideal language for building ML models. You'll learn about supervised and unsupervised learning, deep learning, neural networks, and more. Start with the basics of linear regression and classification, then move on to more complex models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Data Structures and Algorithms (DSA)...
Read more