Data Models and Validation

Pydantic, dataclasses, and data validation patterns.

Overview

Master data modeling patterns.

Dataclasses

from dataclasses import dataclass, field
from typing import List

@dataclass
class User:
    name: str
    email: str
    age: int
    hobbies: List[str] = field(default_factory=list)
    
    def greet(self):
        return f"Hello, {self.name}!"

user = User("Alice", "alice@example.com", 25, ["reading", "coding"])
print(user.greet())

Pydantic Models

from pydantic import BaseModel, EmailStr, validator
from typing import Optional

class User(BaseModel):
    name: str
    email: EmailStr
    age: int
    bio: Optional[str] = None
    
    @validator('age')
    def age_must_be_positive(cls, v):
        if v < 0:
            raise ValueError('Age must be positive')
        return v
    
    @validator('name')
    def name_must_not_be_empty(cls, v):
        if not v.strip():
            raise ValueError('Name cannot be empty')
        return v.strip()

# Usage
user = User(name="Alice", email="alice@example.com", age=25)
print(user.dict())

Validation Patterns

from pydantic import BaseModel, validator
from typing import List

class Order(BaseModel):
    items: List[str]
    total: float
    
    @validator('total')
    def total_must_be_positive(cls, v):
        if v <= 0:
            raise ValueError('Total must be positive')
        return round(v, 2)
    
    @validator('items')
    def items_cannot_be_empty(cls, v):
        if not v:
            raise ValueError('Items list cannot be empty')
        return v

order = Order(items=["apple", "banana"], total=5.99)
print(order)

Practice

Create a Pydantic model for a complex form with validation.