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https://github.com/kaliv0/pypermissive
Validation library in Python, modeled after Pydantic
https://github.com/kaliv0/pypermissive
pydantic-v2 python-validator validation-library
Last synced: 10 days ago
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Validation library in Python, modeled after Pydantic
- Host: GitHub
- URL: https://github.com/kaliv0/pypermissive
- Owner: kaliv0
- License: mit
- Created: 2024-08-01T21:38:22.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-11-06T21:50:35.000Z (10 days ago)
- Last Synced: 2024-11-06T22:34:21.121Z (10 days ago)
- Topics: pydantic-v2, python-validator, validation-library
- Language: Python
- Homepage: https://pypi.org/project/pypermissive/
- Size: 101 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
---
# PyPermissive
[](https://github.com/kaliv0/pypermissive/actions/workflows/ci.yml)
[](https://pypi.org/project/pypermissive/)
[](https://pepy.tech/projects/pypermissive)
[](https://github.com/kaliv0/pypermissive/blob/main/LICENSE)
Validation library in Python, modeled after Pydantic
## Example
Inherit from BaseModel and describe required types.
PyPermissive supports validation for primitive types:
```python
class Employee(BaseModel):
employee_id: int
name: str
salary: float
elected_benefits: bool = False
employee = Employee(
employee_id=1,
name="Foo Bar",
salary=123_000.00,
elected_benefits=True,
)
```
collections:
```python
class Book(BaseModel):
characters: dict[str, str]
chapters: list[str]
book = Book(
characters={"Pelleas": "he", "Melisande": "she"},
chapters=["Beginning", "Middle", "End"]
)
```
unions, classes and fields.
Fields are similar to pydantic with one caveat: you need to give value type explicitly:
```python
class User(BaseModel):
name: Field(type=str, default="Jimmie", frozen=True)
age: Field(type=int, gt=18, lt=35)
id: Field(type=UUID, default_factory=uuid4)
email: Field(type=str, pattern=r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+[.][a-zA-Z0-9-.]+$")
nickname: Field(type=str, min_length=6, max_length=12)
PIN: Field(type=str, field_validator=lambda x: x.isdigit())
```
You can also use decorators:
@ComputedField (invoke only from instances) and @ComputedClassField (invoke both on class and instance level)
```python
class Thesis:
BAZZ = ["1", "2", "3"]
def __init__(self):
self.fizz = [1, 2, 3, 4, 5]
self.buzz = [6, 7, 8, 9]
@ComputedField
def foo(self):
return [val for val in itertools.product(self.fizz, self.buzz)]
@ComputedClassField
def bar(self):
return list(itertools.permutations(self.BAZZ))
```
The library supports @validate_call that checks both argument and return types:
```python
@validate_call
def some_func(delimiter: str, count: int, numbers: list[int]) -> str:
return (delimiter * count).join([str(d) for d in numbers])
```