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Show usage of Python Pydantic class with FastAPI

Using Pydantic with FastAPI for Data Validation 

FastAPI is a modern Python framework that makes building APIs fast and easy. It integrates with Pydantic to provide automatic data validation and serialization. In this post, we'll explore how to use Pydantic models inside a FastAPI app.

1️⃣ Install FastAPI & Uvicorn

Before we start, install FastAPI and Uvicorn using pip:

pip install fastapi uvicorn pydantic
  • FastAPI: The API framework.
  • Uvicorn: ASGI server to run the FastAPI app.

2️⃣ Creating a Simple FastAPI App

Let's create a basic FastAPI app that accepts user data, validates it, and returns a response.

📌 Code: FastAPI with Pydantic

from fastapi import FastAPI
from pydantic import BaseModel, EmailStr

app = FastAPI()

# Define a Pydantic model
class User(BaseModel):
    id: int
    name: str
    age: int
    email: EmailStr  # Validates email format

# Endpoint to create a user
@app.post("/users/")
async def create_user(user: User):
    return {"message": "User created successfully", "user": user}

# Run using: uvicorn filename:app --reload

3️⃣ Running the FastAPI App

Save the file as main.py and run:

uvicorn main:app --reload

Now, open your browser and visit:

4️⃣ Testing API with JSON Data

Use Swagger UI or Postman to send a POST request to /users/ with JSON:

{
  "id": 1,
  "name": "Alice",
  "age": 25,
  "email": "alice@example.com"
}

✅ Success Response:

{
  "message": "User created successfully",
  "user": {
    "id": 1,
    "name": "Alice",
    "age": 25,
    "email": "alice@example.com"
  }
}

🚫 Validation Error (Invalid Email):

{
  "detail": [
    {
      "loc": ["body", "email"],
      "msg": "value is not a valid email address",
      "type": "value_error.email"
    }
  ]
}

5️⃣ Adding Custom Validation in Pydantic

We can enforce custom rules using @validator.

📌 Example: Restrict Age to 18+

from pydantic import validator

class User(BaseModel):
    id: int
    name: str
    age: int
    email: EmailStr

    @validator("age")
    def validate_age(cls, value):
        if value < 18:
            raise ValueError("Age must be 18 or older")
        return value

Now, if a user under 18 tries to register, they’ll get a validation error.

6️⃣ Using Nested Pydantic Models

For complex objects, we can nest Pydantic models.

📌 Example: User with Address

from typing import List

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

class User(BaseModel):
    id: int
    name: str
    age: int
    email: EmailStr
    address: Address  # Nested Pydantic Model

@app.post("/users/")
async def create_user(user: User):
    return {"message": "User created", "user": user}

Now, JSON requests must include an address:

{
  "id": 2,
  "name": "Bob",
  "age": 30,
  "email": "bob@example.com",
  "address": {
    "street": "456 Elm St",
    "city": "Los Angeles",
    "zip_code": "90001"
  }
}

7️⃣ Returning Pydantic Models as API Responses

You can enforce structured responses with response_model.

📌 Example:

@app.post("/users/", response_model=User)
async def create_user(user: User):
    return user

Now, FastAPI ensures that responses match the User model.

🎯 Conclusion

FastAPI + Pydantic provides: ✅ Automatic data validation
✅ Easy JSON serialization
✅ Swagger UI for testing
✅ Custom validators for business rules


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