İç İçe Modeller ve Özyinelemeli Yapılar

Understanding Nested Pydantic Models

Defining Your First Nested Model

from pydantic import BaseModel

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

class User(BaseModel):
    name: str
    email: str
    address: Address

# We'll see it in action next!

Instantiating Nested Pydantic Models

from pydantic import BaseModel

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

class User(BaseModel):
    name: str
    email: str
    address: Address

if __name__ == "__main__":
    user_data = {
        "name": "Alice Wonderland",
        "email": "alice@example.com",
        "address": {
            "street": "123 Rabbit Hole",
            "city": "Wonderland",
            "zip_code": "90210"
        }
    }
    user = User(**user_data)
    print(f"User: {user.name}")
    print(f"Lives in: {user.address.city}")

    # Pydantic will validate nested data:
    try:
        User(name="Bob", email="b@example.com", address={"street": "Main"})
    except Exception as e:
        print(f"\nValidation Error (expected): {e}")

Lists of Nested Pydantic Models

from typing import List
from pydantic import BaseModel

class Skill(BaseModel):
    name: str
    level: int # e.g., 1-5

class Developer(BaseModel):
    name: str
    skills: List[Skill]

if __name__ == "__main__":
    dev_data = {
        "name": "Grace Hopper",
        "skills": [
            {"name": "Python", "level": 5},
            {"name": "SQL", "level": 4},
            {"name": "Algorithms", "level": 3}
        ]
    }
    developer = Developer(**dev_data)
    print(f"Developer: {developer.name}")
    print("Skills:")
    for skill in developer.skills:
        print(f"- {skill.name} (Level: {skill.level})")

Unlocking Recursive Pydantic Models

The Challenge of Self-Referencing Types

Modeling an Org Chart with Recursion

from typing import List, Optional
from pydantic import BaseModel

class Employee(BaseModel):
    name: str
    title: str
    # 'Employee' is a forward reference to itself
    manager: Optional['Employee'] = None
    subordinates: List['Employee'] = []

# We'll build an org chart in the next scene!

Building a Recursive Data Structure

from typing import List, Optional
from pydantic import BaseModel

class Employee(BaseModel):
    name: str
    title: str
    manager: Optional['Employee'] = None
    subordinates: List['Employee'] = []

if __name__ == "__main__":
    # Create employees
    ceo = Employee(name="Mr. Boss", title="CEO")
    manager_a = Employee(name="Ms. Lead", title="Manager A", manager=ceo)
    dev_1 = Employee(name="Dev One", title="Developer", manager=manager_a)
    dev_2 = Employee(name="Dev Two", title="Developer", manager=manager_a)

    # Link subordinates (Pydantic can also do this from structured data dicts)
    manager_a.subordinates.extend([dev_1, dev_2])
    ceo.subordinates.append(manager_a)

    print(f"Org Chart: {ceo.name} ({ceo.title})")
    for sub in ceo.subordinates:
        print(f"  - {sub.name} ({sub.title})")
        for dev in sub.subordinates:
            print(f"    - {dev.name} ({dev.title})")

    # You can also parse a dictionary directly:
    org_data = {
        "name": "Top CEO", "title": "CEO",
        "subordinates": [
            {
                "name": "Mid Manager", "title": "Manager",
                "subordinates": [
                    {"name": "Junior Dev", "title": "Developer"}
                ]
            }
        ]
    }
    full_org = Employee(**org_data)
    print(f"\nParsed Org: {full_org.name} -> {full_org.subordinates[0].name} -> {full_org.subordinates[0].subordinates[0].name}")

Validation in Recursive Models

Nested & Recursive Model Check

Recap: Mastering Complex Data Structures