Quick note on Pydantic

Pydantic is a Python library for data validation and settings management using Python type annotations.

It guarantees the types and shapes of data at runtime, making your code more robust and self-documenting.

Use cases:

• API request/response validation (e.g., FastAPI)

• Configuration management

• User input parsing

• Data cleaning from external sources (JSON, YAML, etc.)

pip install pydantic

Basic Model Usage

Create a BaseModel subclass

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str
    is_active: bool = True  # default value

Create an instance

# Create instance 
user = User(id=1, name="Alice", email="alice@example.com")
print(user)

Pydantic will automatically:

• Convert types ("1" to 1)

• Validate required fields

• Use default values where applicable

Example

user = User(id="42", name="Bob", email="bob@example.com")
print(user.id)  # 42 — automatically cast from string to int

Field Types and Constraints

You can add field constraints with Field():

from pydantic import Field

class Product(BaseModel):
    name: str
    price: float = Field(..., gt=0)
    description: str = Field(default="N/A", max_length=100)

product = Product(name="Laptop", price=-999.99)

Pydantic will raise an error

validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
pydantic_core._pydantic_core.ValidationError: 1 validation error for Product
price
  Input should be greater than 0 [type=greater_than, input_value=-999.99, input_type=float]
    For further information visit https://errors.pydantic.dev/2.11/v/greater_than

Common Constraints

• gt, ge - Greater than, Greater or equal

• lt, le - Less than, Less or equal

• max_length - For strings

• min_items - For lists

• regex - Match a pattern

• default_factory - Create dynamic defaults

Nested Models

class Address(BaseModel):
    city: str
    country: str

class Employee(BaseModel):
    name: str
    address: Address

emp = Employee(name="Jane", address={"city": "Berlin", "country": "Germany"})
print(emp.address.city)  # "Berlin"

Aliases and Field Mapping

Sometimes JSON keys don't match your Python naming:

class Book(BaseModel):
    title: str = Field(..., alias='book_title')

book = Book(book_title="Deep Work")
print(book.title)

Custom Validators

from pydantic import validator

class CustomUser(BaseModel):
    name: str
    email: str

    @validator("email")
    def validate_email(cls, v):
        if "@" not in v:
            raise ValueError("Invalid email")
        return v

custom_user = CustomUser(name="Alice", email="alice#example.com")

Pydantic will raise error

validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
pydantic_core._pydantic_core.ValidationError: 1 validation error for CustomUser
email
  Value error, Invalid email [type=value_error, input_value='alice#example.com', input_type=str]

Parsing & Serialization

Parsing from JSON/dict

data = {"id": "100", "name": "Alice", "email": "alice@example.com"}
user = User.model_validate(data)
print(user)

Serialization to JSON

print(user.model_dump())         # as dict
print(user.model_dump_json())    # as JSON string

This was a quick note on common Pydantic use cases. Happy coding!!