Python Type Hints Cheat Sheet for Working Developers

A developer published a practical Python type hints cheat sheet covering scalar and collection annotations, function signatures with return types, and advanced patterns like Protocol for structural subtyping and TypedDict for structured JSON payloads. The guide recommends running mypy --strict in CI/CD pipelines and adopting modern built-in generics such as list[str] and str | None on Python 3.10+, with examples drawn from typing Anthropic SDK calls.

3 min read · 512 words Python’s type hinting system has evolved from a quiet PEP into an indispensable tool for growing codebases. If you are still writing raw Python without type hints, you are missing out on instant IDE feedback, bulletproof refactoring, and self-documenting code. Let's cut the theory. Here is a practical, no-fluff cheat sheet for the type hints you will actually use in production every day. Keep it simple. Scalars use standard Python types str , int , float , bool , while collections require imports from the typing module or modern built-in generics supported in Python 3.9+ . python from typing import Dict, List, Optional, Tuple, Union Scalars name: str = "Claude" age: int = 5 is active: bool = True price: float = 99.9 Collections tags: List str = "ai", "llm", "python" coordinates: Tuple float, float = 37.7749, -122.4194 metadata: Dict str, Union str, int = {"model": "claude-opus-5", "tokens": 1000000} Optional values equivalent to Type | None middle name: Optional str = None Functions are where type hints shine brightest. Always type your arguments and your return values. Use - None for functions that perform side effects without returning data. python from typing import Callable, Sequence def calculate cost tokens: int, rate per million: float - float: """Calculate API cost based on token usage.""" return tokens / 1 000 000 rate per million def process batch items: Sequence str , callback: Callable str , None - None: """Process a sequence of items and execute a callback on each.""" for item in items: callback item Real-world usage with the modern Anthropic SDK import anthropic def generate completion prompt: str, model id: str = "claude-opus-5" - str: client = anthropic.Anthropic response = client.messages.create model=model id, max tokens=4000, messages= {"role": "user", "content": prompt} , Extract text from the response content blocks safely return "".join block.text for block in response.content if block.type == "text" When your codebase grows, strict class hierarchies can slow you down. Use Protocol for structural subtyping duck typing with type safety and TypedDict for structured JSON payloads. python from typing import Protocol, TypedDict Protocol: Any object with a 'generate' method matches this type class LLMProvider Protocol : def generate self, prompt: str - str: ... TypedDict: Perfect for validating API payloads and config dictionaries class APIConfig TypedDict : model: str max tokens: int thinking enabled: bool def configure request config: APIConfig - None: print f"Configuring request for model: {config 'model' }" Valid dictionary matching TypedDict structure my config: APIConfig = { "model": "claude-sonnet-5", "max tokens": 8000, "thinking enabled": True } configure request my config mypy --strict your module.py to your CI/CD pipeline to catch type errors before they hit production. list str over List str and str | None over Optional str if you are running Python 3.10+. Any temporarily—just don't leave it there permanently. What is your favorite advanced typing pattern in Python? Drop a comment below with your go-to trick or headache. If you found this cheat sheet helpful, hit the bookmark button and share it with your team