Skip to main content

Operator Overloading in Python

 

Learning Sections          show

Operator Overloading in Python

Operator overloading in Python allows you to define custom behavior for operators when they are used with user-defined classes. This makes your objects behave more like built-in types, which can lead to more intuitive and readable code. Operator overloading is implemented by defining special methods, also known as magic methods or dunder methods (because they begin and end with double underscores).

For example, to overload the addition operator +, you need to define the __add__ method in your class.

Example: Overloading the Addition Operator

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 __repr__(self):
        return f"Point({self.x}, {self.y})"

p1 = Point(1, 2)
p2 = Point(3, 4)
p3 = p1 + p2

print(p3)  # Output: Point(4, 6)
    
Explanation:

In the example above, the __add__ method is defined to overload the addition operator +. When we create two instances of the Point class and add them using p1 + p2, Python calls the __add__ method, which returns a new Point instance with the coordinates summed.

Other Common Operators

Here are some other common operators and their corresponding magic methods:

  1. + : __add__(self, other)
  2. - : __sub__(self, other)
  3. * : __mul__(self, other)
  4. / : __truediv__(self, other)
  5. // : __floordiv__(self, other)
  6. % : __mod__(self, other)
  7. ** : __pow__(self, other)
  8. == : __eq__(self, other)
  9. != : __ne__(self, other)
  10. > : __gt__(self, other)
  11. < : __lt__(self, other)
  12. >= : __ge__(self, other)
  13. <= : __le__(self, other)
Example: Overloading Comparison Operators

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

    def __eq__(self, other):
        return self.age == other.age

    def __lt__(self, other):
        return self.age < other.age

person1 = Person("Alice", 30)
person2 = Person("Bob", 25)
person3 = Person("Charlie", 30)

print(person1 == person3)  # Output: True
print(person1 < person2)   # Output: False
    
Explanation:

In this example, we overload the equality operator == using the __eq__ method and the less than operator < using the __lt__ method. This allows us to compare Person objects based on their age attribute.

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...

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 (...

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)...