Python Error: Understanding the Order of Property Functions

What Will You Learn?

In this tutorial, you will delve into the intricate world of property functions in Python. By exploring how property functions work and why their order is crucial, you will enhance your ability to troubleshoot errors effectively and craft more robust code.

Introduction to the Problem and Solution

When dealing with property functions in Python, it’s essential to grasp the significance of their order. The sequence in which property functions are defined can significantly impact the behavior of your code. By understanding the interplay between these functions, you can preemptively address errors and fortify your codebase.

Let’s uncover an error related to the arrangement of property functions within a class and provide a viable solution to rectify it.


class Rectangle:
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

    def perimeter(self):
        return 2 * (self.width + self.height)

# Instantiate a Rectangle object
rect = Rectangle(5, 10)

print(rect.area)       # Output: 50 
print(rect.perimeter)  # Output: 30 

# Copyright PHD

(Code snippet sourced from


In Python, @property decorator empowers developers to define properties that mimic attributes but are computed dynamically within a class. The order in which these property functions are declared holds paramount importance as each subsequent function relies on its predecessors for accurate computation.

In our example above: – The area property hinges on width and height. – The perimeter property also relies on width and height.

Altering the order of these properties or accessing them before initializing width or height may lead to erroneous results or failures due to unmet dependencies. Therefore, meticulous consideration of the order of properties is imperative when working with multiple properties in Python classes.

    1. How does changing the order of @property decorators affect my code? Changing the order of @property decorators can influence how your properties are calculated since each may depend on others.

    2. Can I have circular dependencies between @property methods? Yes, circular dependencies are permissible as long as they’re correctly defined without causing infinite recursion.

    3. Why do we use @property in Python classes? The @property decorator enables us to define getter/setter methods as attributes for seamless access similar to regular attributes.

    4. Is there any limit on how many @properties I can have in a class? While there’s no strict limit, an excessive number might indicate poor design and could impede code maintainability.

    5. How do I handle exceptions raised by @property methods? Exception handling within @property methods can be managed using try-except blocks akin to regular methods.

    6. Can I delete or modify values using @properties directly? Yes, setter methods can be defined using @your_property_name.setter decorator for altering values through properties.

    7. Are there performance implications when using @properties extensively? Accessing properties incurs overhead compared to simple attribute access; however, unless performance-critical scenarios arise, concerns about it should be minimal.

    8. Do all instances share the same set of properties defined by an object’s class? No. Each instance possesses its own set of instance variables even if derived from shared class-level definitions like @classmethods or @staticmethods.

    9. How does inheritance work with parent class’ @properties overridden by child classes’ new ones? Child classes supersede parent class behaviors including inherited @(class-)methods ensuring correct method resolution based on MRO (Method Resolution Order).


Comprehending how property functions interact within Python classes is pivotal for crafting dependable codebases. By meticulously considering the ordering of these functions based on their interdependencies, you can steer clear of errors and foster the development of more sustainable software solutions.

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