Скачать или смотреть Understanding Why dataclass Creates Shared Variables and How to Fix It

Learn how to properly use Python's `dataclass` for creating unique instance variables, avoiding common pitfalls with mutable defaults.
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Understanding Why dataclass Creates Shared Variables and How to Fix It

The Problem with Default Values in Python dataclasses

When you work with Python's dataclass, you might run into an unexpected behavior regarding default values for attributes. Consider the following example where a shared mutable object causes confusion when multiple instances of a class are created:

[[See Video to Reveal this Text or Code Snippet]]

What's Going Wrong?

The issue arises when the Container class is defined. In this code, a single instance of the VS object is created once when the Container class is defined. As a result, this same instance is shared across all instances of the Container class (c1 and c2).

Key Issues:

Mutable Objects: The attribute a is an instance of a user-defined object (VS), which is mutable. Therefore, changing the value of a in one instance (c1.a.v) will change it for all instances (c2.a.v).

Unexpected Behavior: The outputs confirm that modifications to the attributes cause other instances to reflect those changes, contrary to what one would typically expect from instance variables.

The Solution: Using default_factory

To ensure that each instance of Container gets its own unique VS object, you should use the default_factory of the field function. This approach allows you to create a fresh instance of VS each time a Container is initialized.

Updated Code Example:

Here’s how to implement this:

[[See Video to Reveal this Text or Code Snippet]]

Expected Output:

When running the fixed code, you should see:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By understanding the nuances of Python's dataclass, specifically how mutable default values behave, you can create robust classes that work as intended. Using the field with default_factory allows for the generation of new instances, thereby avoiding unintended shared states.

This method is essential for maintaining data integrity within your classes. Now, you can confidently use dataclass without running into the pitfalls of shared mutable defaults.