Pydantic
35 bites tagged Pydantic — interview questions with model answers, and 60-second explainers.
Pydantic: Reusable Validation with Annotated Types
Pydantic's `Annotated` attaches validation logic directly to a type, making it reusable. Define a custom type like `SquareNumber` once and apply it to any model field, ensuring consistent validation without repeating code.
FastAPI: Validating Models with Pydantic's Field
Pydantic's `Field` adds guardrails directly to your data model's attributes. Use it to enforce constraints like string length (`max_length=50`) or numeric ranges (`gt=0`), making your models self-validating.
FastAPI: Managing Environment-Specific Settings
Treat app configuration like a contract, not hardcoded values. Pydantic Settings defines required variables (like API keys) and loads them from the environment, preventing you from shipping dev settings to production.
Pydantic BaseSettings: Typed, Layered Configuration
Pydantic's BaseSettings treats configuration as typed data, not just strings. It automatically loads and validates settings from environment variables, .env files, and secrets stores into a Python object.
Pydantic Computed Fields: Serialize Derived Values
A Pydantic computed field makes a derived value, like an area from width and length, part of your model's serialized output. Use it to include calculated attributes when calling `.model_dump()`.
Pydantic: Configuring Models with `model_config`
Think of `model_config` as the settings panel for your Pydantic models, letting you change validation rules like string length or immutability. Use it to enforce global constraints or make models immutable. The footgun is using the old `class Config:` from V1.
Pydantic's Data Coercion: From Raw Data to Python Types
Pydantic automatically converts raw data, like strings from a JSON request, into the Python types you declare. It's how FastAPI turns a JSON body into a typed Python object.
Nested Pydantic Models: Composing Complex Data
Use a Pydantic model as a field type inside another to build complex, nested structures. This is essential for modeling JSON with sub-objects, like a user with an address.
Pydantic: Required vs. Optional Fields
In Pydantic, a field is required by default. To make it optional, you must provide a default value, like `name: str = "guest"` or `age: int | None = None`. This is key for flexible API request bodies.
FastAPI Response Models: Shape Your API's Output
A FastAPI `response_model` defines your API's output shape, acting as a data filter and automatic documentation generator. Use it to prevent data leaks and provide clear schemas.
FastAPI: Pydantic for Robust Request Bodies
A Pydantic model is a contract for your API's request body. It tells FastAPI what data to expect, automatically converting incoming JSON into a typed Python object. Use this for any POST or PUT endpoint. The footgun is declaring path params in the body model.
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