Differences between BaseModel and @dataclass not expected based on documentation #710
Description
Documentation Update Request
For bugs/questions:
- OS: Linux
- Python version: 3.7.3 | packaged by conda-forge
- Pydantic version: 0.31.0
I liked the idea of using a dataclass instead of subclassing from BaseModel, so I tried changing the very first example from the docs to use dataclass instead of BaseModel and it fails.
from datetime import datetime
from typing import List
# from pydantic import BaseModel
from pydantic.dataclasses import dataclass
# class User(BaseModel):
@dataclass
class User:
id: int
name = 'John Doe'
signup_ts: datetime = None
friends: List[int] = []
external_data = {'id': '123', 'signup_ts': '2017-06-01 12:22', 'friends': [1, '2', b'3']}
user = User(**external_data)
print(user)
# > User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]
print(user.id)
# > 123Result:
Traceback (most recent call last):
File "my_pydantic_test.py", line 7, in <module>
@dataclass
File "pydantic/dataclasses.py", line 128, in pydantic.dataclasses.dataclass
# +-------+-------+-------+--------+--------+
File "pydantic/dataclasses.py", line 123, in pydantic.dataclasses.dataclass.wrap
# | | |
File "pydantic/dataclasses.py", line 77, in pydantic.dataclasses._process_class
# +--- frozen= parameter
File "/path/to/python/lib/python3.7/dataclasses.py", line 834, in _process_class
for name, type in cls_annotations.items()]
File "/path/to/python/lib/python3.7/dataclasses.py", line 834, in <listcomp>
for name, type in cls_annotations.items()]
File "/path/to/python/lib/python3.7/dataclasses.py", line 727, in _get_field
raise ValueError(f'mutable default {type(f.default)} for field '
ValueError: mutable default <class 'list'> for field friends is not allowed: use default_factory
I realize that this error is coming from the Std. Lib. dataclasses module, not pydantic. However, based on the language in the dataclasses section of the documentation I had expected what anything I could do with BaseModel I could do with dataclass as well.
If you don’t want to use pydantic’s BaseModel you can instead get the same data validation on standard dataclasses (introduced in python 3.7).
You can use all the standard pydantic field types and the resulting dataclass will be identical to the one created by the standard library dataclass decorator.
Can I suggest that there be a note or warning to the user that there are certain restrictions associated with using a dataclass that are not present when using BaseModel (such as not being able to use mutable defaults, as well as #484 and #639)?