Understanding Method Resolution Order (MRO) for Python Inheritance

2024-05-13

Here's how super() works in multiple inheritance:

  1. Method Resolution Order (MRO): When a class inherits from multiple parents, Python creates a special order called the Method Resolution Order (MRO). This order determines which parent class's method is called first in case of conflicts. The MRO lists the child class itself first, followed by its direct parents, then their parents, and so on.

  2. Resolving Parent Calls: When you call super() in a child class method, it resolves to the next class in the MRO. This means depending on where the call is made and the order of inheritance, super() might refer to a different parent class.

For instance, consider this code:

class A:
    def __init__(self):
        print("In A init")

    def methodA(self):
        print("Method A from A")


class B:
    def __init__(self):
        print("In B init")

    def methodB(self):
        print("Method B from B")


class C(A, B):
    def __init__(self):
        super().__init__()  # Call to A's init first
        print("In C init")

    def methodC(self):
        print("Method C from C")


c = C()
c.methodA()  # Calls A's method
c.methodB()  # Calls B's method
c.methodC()  # Calls C's method

In this example, the MRO for C is [C, A, B, object]. So, when super().__init__() is called in C's constructor, it initializes A first (as A is the next class in the MRO).

Key Points to Remember:

  • The order of classes in the inheritance list affects which parent class super() refers to first.
  • super() can also take an explicit first argument, which should be the child class itself. The second argument can be an object instance. This usage is less common in multiple inheritance.
  • Using super() helps ensure proper initialization of all parent classes in a multiple inheritance hierarchy.

By understanding how super() works with MRO, you can write cleaner and more maintainable code when dealing with multiple inheritance in Python.




Example 1: Basic Call Order

This code shows how super() calls the parent class constructors in the order they are inherited:

class Animal:
    def __init__(self):
        print("Animal is created.")

class Mammal(Animal):
    def __init__(self):
        super().__init__()  # Call Animal's init first
        print("Mammal is created.")

class Dog(Mammal):
    def __init__(self, name):
        super().__init__()  # Call Mammal's init (which calls Animal's init)
        self.name = name
        print("Dog is created.")

dog = Dog("Buddy")

Output:

Animal is created.
Mammal is created.
Dog is created.

Here, Dog inherits from Mammal which inherits from Animal. Calling super().__init__() in each class constructor ensures all parent classes are initialized in the correct order.

Example 2: Accessing Specific Parent Methods

This code demonstrates how super() can be used to call a specific parent class method:

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

    def area(self):
        return self.width * self.width  # Base area calculation

class ColoredShape(Shape):
    def __init__(self, width, color):
        super().__init__(width)  # Call Shape's init
        self.color = color

    def get_info(self):
        return f"Colored shape with area {super().area()} and color {self.color}"

red_square = ColoredShape(5, "Red")
print(red_square.get_info())
Colored shape with area 25 and color Red

Here, ColoredShape inherits from Shape. Inside get_info(), super().area() calls the area() method from the Shape class to calculate the base area.

Remember, these are just a few examples. super() offers flexibility in handling multiple inheritance scenarios. Make sure to understand the MRO (Method Resolution Order) to ensure your code calls the intended parent class methods.




  1. Explicit Calls to Parent Constructors:

Instead of using super(), you can explicitly call each parent class constructor within the child class constructor. This approach works but can become cumbersome and less readable, especially with deep inheritance hierarchies.

Here's an example:

class A:
    def __init__(self):
        print("In A init")

class B:
    def __init__(self):
        print("In B init")


class C(A, B):
    def __init__(self):
        A.__init__(self)  # Explicit call to A's init
        B.__init__(self)  # Explicit call to B's init
        print("In C init")

c = C()

This code achieves the same initialization order as the first example using super(), but it's less concise.

  1. Mixin Classes (Composition):

In some cases, you might be able to refactor your inheritance structure to use mixin classes instead of multiple inheritance. Mixin classes are designed to provide specific functionalities that can be reused across different classes. Instead of inheriting from multiple classes, the child class can have these mixin classes as attributes and call their methods directly.

This approach promotes better code organization and avoids the complexities of multiple inheritance, especially the "diamond problem" (where two parent classes have the same method).

Here's a simplified example using mixin classes:

class Drawable:
    def draw(self):
        print("Drawing an object")

class Colored:
    def __init__(self, color):
        self.color = color

    def get_color(self):
        return self.color

class RedCircle(Drawable, Colored):
    def __init__(self):
        Colored.__init__(self, "Red")  # Explicit call to Colored's init
        # No need to call Drawable's init as it doesn't have state

    def draw(self):
        super().draw()  # Call Drawable's draw method (assuming another Drawable class exists)
        print(f"Drawing a {self.get_color()} circle")

red_circle = RedCircle()
red_circle.draw()

While mixin classes can be a good alternative, they might not always be a perfect fit depending on your specific use case.

Choosing the Right Approach:

  • Generally, super() is the preferred approach for multiple inheritance as it offers a clean and maintainable way to manage parent class calls.
  • Use explicit calls to parent constructors with caution, as it can lead to less readable code.
  • Consider using mixin classes for code organization and to avoid complex inheritance structures, but ensure it aligns with your design goals.

python multiple-inheritance


Counting Occurrences Efficiently in Pandas using value_counts()

Here's how it works:You call value_counts() on the specific column of the DataFrame that you want to analyze. For instance...


Connecting to MySQL Database from Python Flask Application (Using mysqlclient)

Error Breakdown:ImportError: This exception indicates that Python cannot find the module you're trying to import, in this case...


Efficient Matrix Multiplication in PyTorch: Understanding Methods and Applications

PyTorch and MatricesPyTorch is a popular Python library for deep learning. It excels at working with multi-dimensional arrays called tensors...


Taming the CUDA Out-of-Memory Beast: Memory Management Strategies for PyTorch Deep Learning

Understanding the Error:This error arises when your GPU's memory becomes insufficient to handle the demands of your PyTorch program...


Unlocking the Power of GPUs: A Guide for PyTorch Programmers

PyTorch and GPUsPyTorch is a popular deep learning framework that leverages GPUs (Graphics Processing Units) for faster computations compared to CPUs...


python multiple inheritance