What is the purpose of the @property decorator in Python?

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Answer:

The @property decorator in Python is used to create getter and setter methods for class attributes, allowing them to be accessed and modified as if they were ordinary instance variables, while providing a level of control over their access and modification.

The @property decorator is used to define a method as a "getter" for a class attribute. The method is called whenever the attribute is accessed, allowing for additional processing or validation to be performed. The @property decorator can also be used in conjunction with the @setter decorator to define a method as a "setter" for a class attribute. The setter method is called whenever the attribute is assigned a new value, allowing for additional processing or validation to be performed.

For example, consider the following code:

class Rectangle:
    def __init__(self, width, height):
        self._width = width
        self._height = height
    
    @property
    def width(self):
        return self._width
    
    @width.setter
    def width(self, value):
        if value <= 0:
            raise ValueError("Width must be positive")
        self._width = value
    
    @property
    def height(self):
        return self._height
    
    @height.setter
    def height(self, value):
        if value <= 0:
            raise ValueError("Height must be positive")
        self._height = value
    
    def area(self):
        return self._width * self._height

In this example, the @property decorator is used to define getter and setter methods for the width and height attributes of the Rectangle class. The getter methods return the values of the attributes, while the setter methods validate the input and set the values of the attributes.

By using the @property decorator, the width and height attributes can be accessed and modified as if they were ordinary instance variables, while providing additional control over their access and modification. This makes the code more readable and maintainable, and helps to prevent programming errors by validating input and enforcing constraints on the data.

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