Dataclass Validation with Pydantic — Snippets

Pydantic validates data at runtime using Python type annotations. Define a model, and Pydantic handles parsing, coercion, and error reporting.

Defining a model

models.py

from pydantic import BaseModel, Field, field_validator
from datetime import datetime

class UserCreate(BaseModel):
  name: str = Field(min_length=1, max_length=100)
  email: str
  age: int = Field(ge=0, le=150)
  bio: str | None = None
  created_at: datetime = Field(default_factory=datetime.now)

  @field_validator("email")
  @classmethod
  def validate_email(cls, v: str) -> str:
      if "@" not in v or "." not in v.split("@")[-1]:
          raise ValueError("invalid email format")
      return v.lower().strip()

# Valid — age is coerced from string to int
user = UserCreate(name="Ada", email="ADA@example.com", age="29")
print(user.email)  # "ada@example.com"
print(user.age)    # 29

# Invalid — raises ValidationError with clear messages
try:
  UserCreate(name="", email="bad", age=-1)
except Exception as e:
  print(e)

Nested models

nested.py

class Address(BaseModel):
  street: str
  city: str
  country: str = "US"

class UserProfile(BaseModel):
  user: UserCreate
  address: Address
  tags: list[str] = []

Pydantic validates the entire object graph recursively, giving precise error paths like address.city.