What are the limitations of dataclasses?

What are the limitations of dataclasses in Python?

Dataclasses in Python are great for simplifying class creation, but they do have some limitations. For instance, they are immutable by default, which means you can't change their fields after creation unless you set frozen=False. Additionally, they don't support inheritance as flexibly as regular classes, and you can't easily customize their behavior without additional methods.

Can you give me an example of a limitation?

Sure! One limitation is that dataclasses do not support default values for fields that are mutable types, like lists or dictionaries. If you set a mutable default value, all instances of the dataclass will share that same object, which can lead to unexpected behavior.

Are there any performance issues with dataclasses?

Generally, dataclasses are efficient, but they can introduce some overhead compared to regular classes due to the additional features they provide, like automatic __init__ and __repr__ methods. For most use cases, this overhead is negligible, but in performance-critical applications, it might be a consideration.

How do dataclasses compare to namedtuples?

Dataclasses are more flexible than namedtuples. While namedtuples are immutable and primarily used for simple data structures, dataclasses allow for mutable fields, default values, and more complex behaviors, making them suitable for a wider range of applications.