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Comment by leoff | Hacker News Reader

For my general uses, pydantic and dataclasses are very similar, and I prefer pydantic over dataclass for the following reasons:

1. pydantic by default ignores extra fields - it's useful when I make an API call and want to extract and validate only certain fields from the response, while dataclasses throws errors if you don't specify all fields, and this behavior can't be disabled

2. pydantic is more customizable - I can overwrite the BaseModel if I have a custom need

3. pydantic seems to integrate better with python typing - I'm not sure how to explain this one, but it feels more natural and dynamic

I'm not sure what the performance concerns are, though

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Pydantic is much much much slower than normal dataclasses. However, they have to be because they do validation and dataclasses do not. So, I use normal dataclasses for internal constructs, which need to be fast and pydantic for anything that comes externally, can't be trusted and has to be validated.

Pydantic models also have a model.construct(*src) form which skips validation. According to Pydantic's docs [1], this makes it 30x faster.

[1] https://docs.pydantic.dev/usage/models/#creating-models-with...

What I seriously love about pydantic is the ability to just write the default value of `tags: list[str] = []` and not worry about all of the instances of the class sharing the same single instance of the default list.

Does that work now? I seem to remember having to use `tags: list[str] = Field(default_factory=list)` a lot

Pydantic, yes, you can just set the default initializer to = [] or = {}. I don't think that works with dataclasses yet.

The intent of pydantic is to be a parser of (possibly recursive, with composition of classes) key-value "languages". It could, in principle, evolve in a completely different direction than dataclasses/msgpack/dataclassy, etc.

This is like -- there are many libraries that do PCA, from sklearn to statsmodels to plain scipy to genomics tools. But in a glue-language workflow you should choose a tool for its semantics.

There's also dataclassy, which stays closer to the standard library dataclasses while improving on them.

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