النماذج المتداخلة والبنى التكرارية
عالجوا بفعالية بنى JSON المتداخلة بعمق ونماذج البيانات التكرارية باستخدام Pydantic.
النماذج المتداخلة والبنى التكرارية درس مجاني في FastAPI Backend Development Bootcamp على CoddyKit. هذا هو الدرس 3 من أصل 4. يمكنك قراءة الدرس كاملاً أدناه مجاناً — ثم تمرن عليه مباشرة في المتصفح باستخدام محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7. هذا الدرس جزء من مسار التعلم في FastAPI Backend Development Bootcamp، وتقدمك يتزامن عبر الويب وتطبيق CoddyKit. تتضمن دورة FastAPI Backend Development Bootcamp 4 دروس في المجموع.
بعض أجزاء هذا الدرس لم تُترجم بعد وتظهر باللغة الإنجليزية.
Understanding Nested Pydantic Models
In real-world applications, data is rarely flat. It often has a hierarchical structure, meaning some data points are collections of other data points.
Nested models in Pydantic allow you to define complex data structures by embedding one BaseModel within another. This helps you build robust and well-organized data schemas.
Defining Your First Nested Model
Let's create an Address model and then use it as a field within a User model. Notice how address: Address links the two.
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
Now, let's create an instance of our User model. Pydantic automatically validates the nested Address data, ensuring all required fields are present and correctly typed.
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
You can also have fields that are lists of other Pydantic models. This is common for things like a user having multiple items, or a product having several features.
We use List from the typing module for this.
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
Sometimes, data structures are even more complex: they refer to themselves. This is called a recursive model.
- Think of a comment section where replies are also comments.
- An organizational chart where employees can have managers who are also employees.
- A file system where folders contain other folders.
Pydantic can handle these self-referencing structures elegantly.
The Challenge of Self-Referencing Types
When a model needs to refer to itself, Python faces a "chicken-and-egg" problem: how can you define a type that isn't fully defined yet?
Pydantic solves this using forward references. In Pydantic v2 (and often in v1 with string literals), you can simply use the model's name as a string for the type hint.
Modeling an Org Chart with Recursion
Let's create an Employee model where an employee can have a manager (who is also an Employee) and a list of subordinates (also Employees).
Notice the use of 'Employee' as a string for the type hint to enable the 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
Here's how you can instantiate the Employee model to build a small organizational hierarchy. Pydantic handles the validation of each nested/recursive layer.
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
Just like with nested models, Pydantic's validation engine works seamlessly with recursive structures. It ensures that every level of the hierarchy conforms to the defined model, catching type errors or missing fields.
This makes handling complex, self-referencing data much safer and easier to manage.
Nested & Recursive Model Check
Let's check your understanding of Pydantic's powerful data modeling features.
Recap: Mastering Complex Data Structures
You've learned how to handle complex data with Pydantic!
- Nested models allow you to compose complex data structures from simpler ones, with automatic validation at every level.
- Recursive models enable you to define self-referencing data, perfect for hierarchies like organizational charts or comment threads.
- Pydantic's use of forward references (often string literals) makes defining recursive types straightforward.
These techniques are fundamental for building robust and clear APIs that interact with structured data.
الأسئلة الشائعة
هل درس «النماذج المتداخلة والبنى التكرارية» مجاني؟
نعم — نص درس «النماذج المتداخلة والبنى التكرارية» كامل متاح مجاناً هنا على الويب. لتمرينه بشكل تفاعلي (محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7) وفتح باقي دورة FastAPI Backend Development Bootcamp، انتقل إلى CoddyKit PRO. تتضمن دورة FastAPI Backend Development Bootcamp 4 دروس في المجموع.
ماذا ستتعلم في «النماذج المتداخلة والبنى التكرارية»؟
عالجوا بفعالية بنى JSON المتداخلة بعمق ونماذج البيانات التكرارية باستخدام Pydantic. تتمرن على FastAPI Backend Development Bootcamp مع أكواد عملية تشغلها مباشرة في المتصفح، ومدرس ذكاء اصطناعي متاح 24/7 يجيب على أسئلتك أثناء عملك.
هل أحتاج إلى خبرة سابقة لأبدأ FastAPI Backend Development Bootcamp؟
لا تُشترط خبرة سابقة. FastAPI Backend Development Bootcamp على CoddyKit منظم للمبتدئين حتى المتقدمين، لذا يمكنك البدء من هنا أو من البداية والتقدم بسرعتك الخاصة. هذا هو الدرس 3 من أصل 4.
كم من الوقت يستغرق درس «النماذج المتداخلة والبنى التكرارية»؟
معظم دروس CoddyKit تستغرق حوالي 5–10 دقائق. كل منها موجز وتفاعلي، لذا تحرز تقدماً مستمراً وتستأنف من حيث توقفت عبر الويب والتطبيق.
هل يمكنني كتابة وتشغيل أكواد في درس FastAPI Backend Development Bootcamp هذا؟
نعم. كل درس في FastAPI Backend Development Bootcamp يتضمن محرر أكواد مدمج، لذا تكتب وتشغل أكواداً حقيقية مباشرة في متصفحك وتحصل على تعليقات فورية من الذكاء الاصطناعي — بدون إعداد محلي.
جميع الدروس في هذه الدورة
- التحقق من حقول Pydantic وأدوات التحقق
- أنواع البيانات المخصّصة والإعدادات
- النماذج المتداخلة والبنى التكرارية
- التسلسل باستخدام model_dump والأسماء المستعارة