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Type hints, protocols & validation

Use static contracts to improve maintainability while validating external data at runtime.

Apply this to your Python data tools, PixelFed quality work, n8n integrations or BI delivery when the project needs it.

Understand the idea

Type hints describe expected values to editors and static tools. Python does not automatically reject a string passed into an int-annotated parameter. Validate untrusted CSV, JSON and environment values when they enter the application.

A protocol describes the operations a collaborator must support without requiring a shared implementation. Keep the interface narrow: one read method is easier to fake and reason about than a provider object exposing dozens of unrelated calls.

What to learn

• Annotate collection element types and optional results.
• Use Protocol to describe a small reader interface.
• Distinguish a type checker from runtime parsing and validation.

A small original example

from typing import Protocol, Iterable

class Reader(Protocol):
    def read(self) -> Iterable[dict[str, str]]: ...

Practice on your own data

Type a reader returning Iterable[Order], then inject a fake reader in tests. Add a malformed JSON input test.

Practice this lesson in the Python lab →

Common pitfalls

• Use syntax supported by the selected interpreter; this repo baseline remains Python 3.11+.

Prerequisites

Modules, typing & data classes · OOP, composition & data classes

Sources and further reading

• Python · type hints and protocols ↗Official project documentation
• pytest · mock modules and environments ↗Official project documentation

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