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docs_src/python_types/tutorial011_py39.py
from pydantic import BaseModel class User(BaseModel): id: int name: str = "John Doe" signup_ts: Union[datetime, None] = 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)
Registered: Sun Nov 03 07:19:11 UTC 2024 - Last Modified: Sat Sep 02 15:56:35 UTC 2023 - 492 bytes - Viewed (0) -
docs/ru/docs/tutorial/extra-models.md
У Pydantic-моделей есть метод `.dict()`, который возвращает `dict` с данными модели. Поэтому, если мы создадим Pydantic-объект `user_in` таким способом: ```Python user_in = UserIn(username="john", password="secret", email="john******@****.***") ``` и затем вызовем: ```Python user_dict = user_in.dict() ```
Registered: Sun Nov 03 07:19:11 UTC 2024 - Last Modified: Sun Oct 06 20:36:54 UTC 2024 - 11.5K bytes - Viewed (0) -
docs/pt/docs/tutorial/extra-models.md
Os modelos Pydantic possuem um método `.dict()` que retorna um `dict` com os dados do modelo. Então, se criarmos um objeto Pydantic `user_in` como: ```Python user_in = UserIn(username="john", password="secret", email="john******@****.***") ``` e depois chamarmos: ```Python user_dict = user_in.dict() ```
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docs/zh/docs/tutorial/extra-models.md
```Python user_in = UserIn(username="john", password="secret", email="john******@****.***") ``` 就能以如下方式调用: ```Python user_dict = user_in.dict() ``` 现在,变量 `user_dict`中的就是包含数据的**字典**(变量 `user_dict` 是字典,不是 Pydantic 模型对象)。 以如下方式调用: ```Python print(user_dict) ``` 输出的就是 Python **字典**: ```Python { 'username': 'john', 'password': 'secret',
Registered: Sun Nov 03 07:19:11 UTC 2024 - Last Modified: Sun Oct 06 20:36:54 UTC 2024 - 6.6K bytes - Viewed (0) -
docs/de/docs/tutorial/extra-models.md
Pydantic-Modelle haben eine `.dict()`-Methode, die ein `dict` mit den Daten des Modells zurückgibt. Wenn wir also ein Pydantic-Objekt `user_in` erstellen, etwa so: ```Python user_in = UserIn(username="john", password="secret", email="john******@****.***") ``` und wir rufen seine `.dict()`-Methode auf: ```Python user_dict = user_in.dict() ```
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docs/en/docs/tutorial/extra-models.md
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docs_src/python_types/tutorial011_py310.py
from pydantic import BaseModel class User(BaseModel): id: int name: str = "John Doe" signup_ts: datetime | None = 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)
Registered: Sun Nov 03 07:19:11 UTC 2024 - Last Modified: Sat Sep 02 15:56:35 UTC 2023 - 461 bytes - Viewed (0) -
docs_src/security/tutorial002_an_py39.py
full_name: Union[str, None] = None disabled: Union[bool, None] = None def fake_decode_token(token): return User( username=token + "fakedecoded", email="john@example.com", full_name="John Doe" ) async def get_current_user(token: Annotated[str, Depends(oauth2_scheme)]): user = fake_decode_token(token) return user @app.get("/users/me")
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tests/test_tutorial/test_custom_docs_ui/test_tutorial002.py
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tests/test_tutorial/test_custom_docs_ui/test_tutorial001.py
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