A comprehensive guide to Python type annotations for data science workflows, covering TypedDict, Literal types, Union types, Callable, Protocol, TypeVar, and generics. Explains how static type checkers like mypy and pyright catch errors before runtime, with practical examples for ML pipelines, API responses, and reusable utilities. Also discusses limitations, gradual adoption strategies, and CI integration.
Table of contents
Making structure explicitMaking choice explicitMaking behaviour explicitPractical considerationsReferencesSort: