pydantic-collections
The pydantic-collections package provides BaseCollectionModel class that allows you
to manipulate collections of pydantic models
(and any other types supported by pydantic).
Requirements
- Python>=3.7
- pydantic>=1.8.2,<3.0
Installation
pip install pydantic-collections
Usage
Basic usage
from datetime import datetime
from pydantic import BaseModel
from pydantic_collections import BaseCollectionModel
class User(BaseModel):
id: int
name: str
birth_date: datetime
class UserCollection(BaseCollectionModel[User]):
pass
user_data = [
{'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'},
{'id': 2, 'name': 'Balaganov', 'birth_date': '2020-04-01T12:59:59'},
]
users = UserCollection(user_data)
print(users)
#> UserCollection([User(id=1, name='Bender', birth_date=datetime.datetime(2010, 4, 1, 12, 59, 59)), User(id=2, name='Balaganov', birth_date=datetime.datetime(2020, 4, 1, 12, 59, 59))])
print(users.dict()) # pydantic v1.x
print(users.model_dump()) # pydantic v2.x
#> [{'id': 1, 'name': 'Bender', 'birth_date': datetime.datetime(2010, 4, 1, 12, 59, 59)}, {'id': 2, 'name': 'Balaganov', 'birth_date': datetime.datetime(2020, 4, 1, 12, 59, 59)}]
print(users.json()) # pydantic v1.x
print(users.model_dump_json()) # pydantic v2.x
#> [{"id": 1, "name": "Bender", "birth_date": "2010-04-01T12:59:59"}, {"id": 2, "name": "Balaganov", "birth_date": "2020-04-01T12:59:59"}]
Strict assignment validation
By default BaseCollectionModel has a strict assignment check
...
users = UserCollection()
users.append(User(id=1, name='Bender', birth_date=datetime.utcnow())) # OK
users.append({'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'})
#> pydantic.error_wrappers.ValidationError: 1 validation error for UserCollection
#> __root__ -> 2
#> instance of User expected (type=type_error.arbitrary_type; expected_arbitrary_type=User)
This behavior can be changed via Model Config
Pydantic v1.x
from pydantic_collections import BaseCollectionModel
...
class UserCollection(BaseCollectionModel[User]):
class Config:
validate_assignment_strict = False
Pydantic v2.x
from pydantic_collections import BaseCollectionModel, CollectionModelConfig
...
class UserCollection(BaseCollectionModel[User]):
model_config = CollectionModelConfig(validate_assignment_strict=False)
users = UserCollection()
users.append({'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'}) # OK
assert users[0].__class__ is User
assert users[0].id == 1
Using as a model field
BaseCollectionModel is a subclass of BaseModel, so you can use it as a model field
...
class UserContainer(BaseModel):
users: UserCollection = []
data = {
'users': [
{'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'},
{'id': 2, 'name': 'Balaganov', 'birth_date': '2020-04-01T12:59:59'},
]
}
container = UserContainer(**data)
container.users.append(User(...))
...
Metadata
Release files for pydantic-collections 0.6.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| pydantic_collections-0.6.0-py3-none-any.whl | Python 3 | none | any | Details for pydantic_collections-0.6.0-py3-none-any.whl |
Total release size: 22.6 kB
Release files / pydantic_collections-0.6.0.tar.gz
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