Pydantic Has a Free API — Data Validation That Makes Python Type-Safe

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Pydantic: Data Validation Using Python Type Hints

Pydantic is the most downloaded Python validation library. Define data models with type hints, Pydantic validates at runtime. Used by FastAPI, LangChain, Airflow, and thousands of production apps.

Why Pydantic

Define models with standard Python type hints
Automatic validation and coercion
JSON Schema generation
Serialization/deserialization
50x faster than V1 (written in Rust)

The Free API

from pydantic import BaseModel, Field, EmailStr
from datetime import datetime

class User(BaseModel):
    name: str = Field(min_length=2, max_length=50)
    email: EmailStr
    age: int = Field(ge=0, le=150)
    created_at: datetime = Field(default_factory=datetime.now)
    tags: list[str] = []

# Valid
user = User(name="Alice", email="alice@example.com", age=30)
print(user.model_dump())  # Dict
print(user.model_dump_json())  # JSON string

# Invalid — raises ValidationError
try:
    User(name="A", email="not-email", age=-1)
except Exception as e:
    print(e)  # Detailed error messages

Nested Models

class Address(BaseModel):
    street: str
    city: str
    country: str = "US"

class Company(BaseModel):
    name: str
    address: Address
    employees: list[User]

# Automatic nested validation
company = Company(
    name="Acme",
    address={"street": "123 Main", "city": "NYC"},
    employees=[{"name": "Bob", "email": "bob@acme.com", "age": 25}]
)

Settings Management

from pydantic_settings import BaseSettings

class Settings(BaseSettings):
    database_url: str
    api_key: str
    debug: bool = False
    max_connections: int = 10

    class Config:
        env_file = ".env"

# Reads from environment variables / .env file
settings = Settings()
print(settings.database_url)  # From DATABASE_URL env var

JSON Schema

print(User.model_json_schema())
# Outputs full JSON Schema — use for OpenAPI docs, form generation

Real-World Use Case

An API team had 50 endpoints with manual input validation — scattered if/else checks, inconsistent error messages. Pydantic models: one source of truth for validation, auto-generated docs, consistent errors. Bug reports from bad input dropped 90%.

Quick Start

pip install pydantic
python -c "from pydantic import BaseModel; print(Ready!)"

Resources

Need validated data pipelines? Check out my tools on Apify or email spinov001@gmail.com for custom solutions.

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