python-patterns

Type hints annotate function parameters and return values with their expected types. Use basic types like `int`, `str`, or `list`, generic types like `List[str]`, and advanced types like `Protocol` and `TypedDict` for clearer, more maintainable code that catches errors earlier.

Dataclasses and Pydantic models provide structured, type-safe data containers. Dataclasses offer immutability and default factories; Pydantic adds validation and serialization. Both replace manual `__init__` methods and reduce boilerplate for defining data structures.

Async patterns use `asyncio` for concurrent execution without threading. Define coroutines with `async def`, await asynchronous operations, and use async context managers and generators. This enables efficient I/O-bound operations and responsive applications.

Pytest enables parametrized tests, fixtures for setup and teardown, and comprehensive mocking. Write tests that run multiple input cases, isolate dependencies, and verify behavior without side effects, improving code reliability.

Design custom exceptions for specific error cases and structure `try-except` blocks to handle errors gracefully. This prevents unhandled crashes, provides clear error context, and enables recovery or informative failure messages.

Yes. Python 3.10+ supports union types with `|` syntax, pattern matching with `match`, and improved type annotations. These modern features work seamlessly with type hints, dataclasses, and async patterns for cleaner, more expressive code.