Python Pydantic for Data Validation
Pydantic is the most widely used data validation library for Python. It uses Python type hints to validate data. It is fast, extensible, and plays nicely with IDEs and linters.
Installation
pip install pydanticBasic Usage
Define a model by inheriting from BaseModel and using standard Python type hints.
from pydantic import BaseModel
from typing import List, Optional
class User(BaseModel):
id: int
name: str = 'John Doe'
signup_ts: Optional[str] = None
friends: List[int] = []
external_data = {
'id': '123',
'signup_ts': '2019-06-01 12:22',
'friends': [1, 2, '3'],
}
user = User(**external_data)
print(user.id)
# Output: 123 (converted to int)
print(user.friends)
# Output: [1, 2, 3] (converted elements to int)Error Handling
If validation fails, Pydantic raises a ValidationError.
from pydantic import ValidationError
try:
User(id='not an int', signup_ts='2019-06-01 12:22')
except ValidationError as e:
print(e)Field Validation
You can use Field to add extra validation constraints (like gt for greater than).
from pydantic import BaseModel, Field
class Item(BaseModel):
name: str
price: float = Field(gt=0, description="The price must be greater than zero")
quantity: int = Field(default=1, ge=1)Custom Validators
You can define custom validation logic using the @field_validator decorator.
from pydantic import BaseModel, field_validator
class User(BaseModel):
name: str
@field_validator('name')
@classmethod
def name_must_contain_space(cls, v: str) -> str:
if ' ' not in v:
raise ValueError('must contain a space')
return v.title()Serialization
Pydantic models provide methods to export data.
# Convert to dictionary
print(user.model_dump())
# Convert to JSON string
print(user.model_dump_json())