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tests/test_tutorial/test_schema_extra_example/test_tutorial005_an_py310.py
"summary": "An example with converted data", "description": "FastAPI can convert price `strings` to actual `numbers` automatically", "value": {"name": "Bar", "price": "35.4"}, }, "invalid": {
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Sat Aug 26 18:03:13 GMT 2023 - 6.8K bytes - Viewed (0) -
docs/ko/docs/tutorial/path-params.md
#### *열거형 멤버* 비교 열거형 `ModelName`의 *열거형 멤버*를 비교할 수 있습니다: ```Python hl_lines="17" {!../../../docs_src/path_params/tutorial005.py!} ``` #### *열거형 값* 가져오기 `model_name.value` 또는 일반적으로 `your_enum_member.value`를 이용하여 실제 값(위 예시의 경우 `str`)을 가져올 수 있습니다: ```Python hl_lines="20" {!../../../docs_src/path_params/tutorial005.py!} ``` !!! tip "팁" `ModelName.lenet.value`로도 값 `"lenet"`에 접근할 수 있습니다.
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docs/en/docs/features.md
* Validation for most (or all?) Python **data types**, including: * JSON objects (`dict`). * JSON array (`list`) defining item types. * String (`str`) fields, defining min and max lengths. * Numbers (`int`, `float`) with min and max values, etc. * Validation for more exotic types, like: * URL. * Email. * UUID. * ...and others.
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docs/pt/docs/features.md
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docs/en/docs/tutorial/query-params-str-validations.md
* `min_length` * `max_length` * `pattern` In these examples you saw how to declare validations for `str` values.
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docs_src/schema_extra_example/tutorial005.py
}, }, "converted": { "summary": "An example with converted data", "description": "FastAPI can convert price `strings` to actual `numbers` automatically", "value": { "name": "Bar", "price": "35.4", }, }, "invalid": {
Python - Registered: Sun May 05 07:19:11 GMT 2024 - Last Modified: Sat Aug 26 18:03:13 GMT 2023 - 1.4K bytes - Viewed (0) -
tests/test_tutorial/test_schema_extra_example/test_tutorial005.py
"summary": "An example with converted data", "description": "FastAPI can convert price `strings` to actual `numbers` automatically", "value": {"name": "Bar", "price": "35.4"}, }, "invalid": {
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Sat Aug 26 18:03:13 GMT 2023 - 6.7K bytes - Viewed (0) -
tests/test_tutorial/test_schema_extra_example/test_tutorial005_py310.py
"summary": "An example with converted data", "description": "FastAPI can convert price `strings` to actual `numbers` automatically", "value": {"name": "Bar", "price": "35.4"}, }, "invalid": {
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Sat Aug 26 18:03:13 GMT 2023 - 6.8K bytes - Viewed (0) -
docs/en/docs/async.md
* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time. * **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.
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docs/tr/docs/tutorial/path-params.md
```Python hl_lines="17" {!../../../docs_src/path_params/tutorial005.py!} ``` #### *Enumeration Değerini* Edinelim `model_name.value` veya genel olarak `your_enum_member.value` tanımlarını kullanarak (bu durumda bir `str` olan) gerçek değere ulaşabilirsiniz: ```Python hl_lines="20" {!../../../docs_src/path_params/tutorial005.py!} ``` !!! tip "İpucu"
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