What's the Difference Between Old-Style and New-Style Classes in Python?

Understanding the Distinction Between Old Style and New Style Classes in Python

In Python, there's a fundamental distinction between old style and new style classes. Old style classes were prevalent prior to Python 2.2, whereas new style classes were introduced to enhance the object model and meta-model capabilities.

Old Style Classes

Old style classes were separate from the concept of type. Regardless of the class of an old style instance, its type was always instance. This meant that all old style instances were implemented using a single built-in type, even though they could have different classes.

New Style Classes

New style classes, on the other hand, unify the concepts of class and type. They are user-defined types and share a common type for instances and classes. If x is an instance of a new style class, type(x) usually matches x.__class__.

Benefits of New Style Classes

Introducing new style classes came with several advantages:

• Unified object model with a complete meta-model
• Ability to subclass built-in types
• Introduction of descriptors for computed properties
• Improved method resolution order in case of multiple inheritance

Default Class Style

For compatibility reasons, classes in Python still default to old style by default. However, you can create new style classes by specifying another new style class or the "top-level type" object as its parent.

Python 3 and New Style Classes

In Python 3, new style classes are the only type available. Classes are always new style, regardless of whether you subclass from the object class or not.

Choosing Between Styles

When to use old style versus new style classes depends on your specific needs and compatibility requirements. If you need to maintain compatibility with Python versions prior to 2.2, old style classes might be necessary. However, for new code in Python 2.2 or newer, it's generally recommended to use new style classes to benefit from the unified object model and enhanced features.

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