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docs/fr/docs/advanced/response-directly.md
Par exemple, vous ne pouvez pas mettre un modèle Pydantic dans une `JSONResponse` sans d'abord le convertir en un `dict` avec tous les types de données (comme `datetime`, `UUID`, etc.) convertis en types compatibles avec JSON. Pour ces cas, vous pouvez spécifier un appel à `jsonable_encoder` pour convertir vos données avant de les passer à une réponse : ```Python hl_lines="6-7 21-22"
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docs/fr/docs/tutorial/query-params.md
* `limit` : avec une valeur de `10` Faisant partie de l'URL, ces valeurs sont des chaînes de caractères (`str`). Mais quand on les déclare avec des types Python (dans l'exemple précédent, en tant qu'`int`), elles sont converties dans les types renseignés. Toutes les fonctionnalités qui s'appliquent aux paramètres de chemin s'appliquent aussi aux paramètres de requête : * Support de l'éditeur : vérification d'erreurs, auto-complétion, etc.
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docs/es/docs/advanced/response-directly.md
Por ejemplo, no puedes poner un modelo Pydantic en una `JSONResponse` sin primero convertirlo a un `dict` con todos los tipos de datos (como `datetime`, `UUID`, etc) convertidos a tipos compatibles con JSON. Para esos casos, puedes usar el `jsonable_encoder` para convertir tus datos antes de pasarlos a la respuesta: ```Python hl_lines="4 6 20 21" {!../../../docs_src/response_directly/tutorial001.py!} ``` !!! note "Detalles Técnicos"
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docs/en/docs/advanced/dataclasses.md
In this case, it's a list of `Item` dataclasses. 6. Here we are returning a dictionary that contains `items` which is a list of dataclasses. FastAPI is still capable of <abbr title="converting the data to a format that can be transmitted">serializing</abbr> the data to JSON. 7. Here the `response_model` is using a type annotation of a list of `Author` dataclasses.
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docs/en/docs/tutorial/path-params.md
{"item_id":3} ``` !!! check Notice that the value your function received (and returned) is `3`, as a Python `int`, not a string `"3"`. So, with that type declaration, **FastAPI** gives you automatic request <abbr title="converting the string that comes from an HTTP request into Python data">"parsing"</abbr>. ## Data validation
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docs/fr/docs/tutorial/path-params.md
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tests/test_typing_python39.py
@needs_py310 def test_typing(): types = { list[int]: [1, 2, 3], dict[str, list[int]]: {"a": [1, 2, 3], "b": [4, 5, 6]}, set[int]: [1, 2, 3], # `set` is converted to `list` tuple[int, ...]: [1, 2, 3], # `tuple` is converted to `list` } for test_type, expect in types.items(): app = FastAPI() @app.post("/", response_model=test_type) def post_endpoint(input: test_type):
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tests/test_tutorial/test_schema_extra_example/test_tutorial005_an.py
"tax": 3.2, }, }, "converted": { "summary": "An example with converted data", "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
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docs/en/docs/alternatives.md
the main features needed by API systems is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc. Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For...
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docs/it/docs/index.md
* Query parameters. * Cookies. * Headers. * Form. * File. * <abbr title="detta anche: serialization, parsing, marshalling">Conversione</abbr> dei dati di output: converte dati e tipi di Python a dati per la rete (come JSON): * Converte i tipi di Python (`str`, `int`, `float`, `bool`, `list`, ecc). * Oggetti `datetime`. * Oggetti `UUID`. * Modelli del database. * ...e molto di più.
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