Pydantic partial model

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Pydantic Partial Model

Pydantic is a Python library for data validation and settings management that uses Python type annotations. It allows you to define data models with validation rules, default values, and other constraints.

In Pydantic, a partial model is a model that allows missing fields during model instantiation. This means that when you create an instance of a partial model, you can provide only a subset of the fields defined in the model, and the missing fields will be considered as valid. This can be useful when you want to update existing data objects without having to provide values for all fields.

Example:

    
import datetime
from pydantic import BaseModel, Field

class User(BaseModel):
    id: int
    username: str
    created_at: datetime.datetime = Field(default_factory=datetime.datetime.utcnow)

user_data = {"id": 1, "username": "john_doe"}

# Creating a partial model instance with missing field
partial_user = User(**user_data)

print(partial_user)
# Output: User id=1, username='john_doe', created_at=datetime.datetime(2022, 7, 5, 10, 45, 30)

In the above example, the User model has three fields: id, username, and created_at. The created_at field has a default value set to the current datetime. When creating a partial_user instance, we provide only the id and username fields. The missing created_at field is automatically populated with the default value specified in the model.

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