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docs/fr/docs/project-generation.md
* **Rapide** : Très hautes performances, comparables à **NodeJS** ou **Go** (grâce à Starlette et Pydantic). * **Intuitif** : Excellent support des éditeurs. <abbr title="aussi appelée auto-complétion, autocomplétion, IntelliSense...">Complétion</abbr> partout. Moins de temps passé à déboguer. * **Facile** : Fait pour être facile à utiliser et apprendre. Moins de temps passé à lire de la documentation.
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docs/fr/docs/alternatives.md
Comme les paramètres sont décrits avec des types TypeScript (similaires aux type hints de Python), la prise en charge par l'éditeur est assez bonne.
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docs/fr/docs/python-types.md
Était-ce `upper` ? `uppercase` ? `first_uppercase` ? `capitalize` ? Vous essayez donc d'utiliser le vieil ami du programmeur, l'auto-complétion de l'éditeur. Vous écrivez le premier paramètre, `first_name`, puis un point (`.`) et appuyez sur `Ctrl+Espace` pour déclencher l'auto-complétion. Mais malheureusement, rien d'utile n'en résulte : <img src="/img/python-types/image01.png"> ### Ajouter des types
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docs/en/docs/deployment/index.md
## Deployment Strategies There are several ways to do it depending on your specific use case and the tools that you use. You could **deploy a server** yourself using a combination of tools, you could use a **cloud service** that does part of the work for you, or other possible options.
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docs/tr/docs/features.md
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docs/en/docs/tutorial/dependencies/classes-as-dependencies.md
{!> ../../../docs_src/dependencies/tutorial001.py!} ``` But then we get a `dict` in the parameter `commons` of the *path operation function*. And we know that editors can't provide a lot of support (like completion) for `dict`s, because they can't know their keys and value types. We can do better... ## What makes a dependency Up to now you have seen dependencies declared as functions.
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docs/en/docs/index.md
* **Fewer bugs**: Reduce about 40% of human (developer) induced errors. * * **Intuitive**: Great editor support. <abbr title="also known as auto-complete, autocompletion, IntelliSense">Completion</abbr> everywhere. Less time debugging. * **Easy**: Designed to be easy to use and learn. Less time reading docs. * **Short**: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.
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docs/ja/docs/contributing.md
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docs/en/docs/advanced/settings.md
So, the function below it will be executed once for each combination of arguments. And then the values returned by each of those combinations of arguments will be used again and again whenever the function is called with exactly the same combination of arguments. For example, if you have a function: ```Python @lru_cache def say_hi(name: str, salutation: str = "Ms."):
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docs/en/docs/tutorial/sql-databases.md
...although in this example we are only creating and reading. ### Read data Import `Session` from `sqlalchemy.orm`, this will allow you to declare the type of the `db` parameters and have better type checks and completion in your functions. Import `models` (the SQLAlchemy models) and `schemas` (the Pydantic *models* / schemas). Create utility functions to: * Read a single user by ID and by email. * Read multiple users.
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