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https://github.com/romis2012/pydantic-collections
Collections of pydantic models
https://github.com/romis2012/pydantic-collections
models parsing pydantic python serialization validation
Last synced: 4 days ago
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Collections of pydantic models
- Host: GitHub
- URL: https://github.com/romis2012/pydantic-collections
- Owner: romis2012
- License: apache-2.0
- Created: 2021-10-23T13:13:19.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2024-07-09T10:41:41.000Z (6 months ago)
- Last Synced: 2024-12-15T03:05:40.091Z (11 days ago)
- Topics: models, parsing, pydantic, python, serialization, validation
- Language: Python
- Homepage:
- Size: 49.8 KB
- Stars: 47
- Watchers: 2
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# pydantic-collections
[](https://github.com/romis2012/pydantic-collections/actions/workflows/ci.yml)
[](https://codecov.io/gh/romis2012/pydantic-collections)
[](https://pypi.python.org/pypi/pydantic-collections)
The `pydantic-collections` package provides `BaseCollectionModel` class that allows you
to manipulate collections of [pydantic](https://github.com/samuelcolvin/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
```python
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
```python
...
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
```python
from pydantic_collections import BaseCollectionModel
...
class UserCollection(BaseCollectionModel[User]):
class Config:
validate_assignment_strict = False
```
Pydantic v2.x
```python
from pydantic_collections import BaseCollectionModel, CollectionModelConfig
...
class UserCollection(BaseCollectionModel[User]):
model_config = CollectionModelConfig(validate_assignment_strict=False)
```
```python
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
```python
...
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(...))
...
```