Unlock Efficient Data Validation in Python with Pydantic

Pydantic is a powerful Python library that leverages type hints to provide data validation and settings management.

Installation

Install the library via pip:

pip install pydantic

Simple Example

Define a model by inheriting BaseModel and declare fields with type annotations. Pydantic validates the data when an instance is created.

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

user = User(id=1, name="John Doe", email="[email protected]")
print(user)

The resulting output is shown below:

ValidationError Example

Providing a value of the wrong type triggers a ValidationError:

from pydantic import ValidationError

try:
    user = User(id="one", name="John Doe", email="[email protected]")
except ValidationError as e:
    print(e)

The error message indicates that id must be an integer:

Using Default Values

Optional fields can be declared with typing.Optional and a default value:

from typing import Optional

class User(BaseModel):
    id: int
    name: str
    email: str
    age: Optional[int] = None  # Optional field with a default value of None

user = User(id=1, name="John Doe", email="[email protected]")
print(user.age)  # Output: None

Custom Validators

Use the @validator decorator to enforce custom validation logic:

from pydantic import validator, ValidationError

class User(BaseModel):
    id: int
    name: str
    email: str

    @validator('name')
    def name_must_contain_space(cls, value):
        if ' ' not in value:
            raise ValueError('Name must contain a space')
        return value

try:
    user = User(id=1, name="JohnDoe", email="[email protected]")
except ValidationError as e:
    print(e)

The validation error is displayed below:

Nested Types

Pydantic supports nested models, enabling complex data structures:

class Address(BaseModel):
    street: str
    city: str
    zipcode: str

class User(BaseModel):
    id: int
    name: str
    address: Address

address_data = {'street': '123 Main St', 'city': 'Anytown', 'zipcode': '12345'}
user_data = {'id': 1, 'name': 'John Doe', 'address': address_data}

user = User(**user_data)
print(user)

Resulting output:

Reading Environment Variables

Use BaseSettings to load configuration from a .env file:

from pydantic import BaseSettings

class Settings(BaseSettings):
    app_name: str
    admin_email: str
    class Config:
        env_file = '.env'

settings = Settings()
print(settings.app_name)
print(settings.admin_email)

Example .env file:

APP_NAME=My Awesome App
[email protected]

Serialization and Deserialization

Models can be converted to dictionaries or JSON and reconstructed from them:

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

user = User(id=1, name="John Doe", email="[email protected]")
user_dict = user.model_dump()
print(user_dict)
user_json = user.model_dump_json()
print(user_json)
new_user = User(**user_dict)
print(new_user)

Output of the serialization steps is shown below:

Conclusion

Pydantic provides a robust validation and parsing mechanism for Python through type annotations. It simplifies data integrity checks, improves code readability, and is a valuable tool for both simple applications and complex systems.