1. **Readability**: Type hints make function signatures self-documenting. A reader immediately knows what types a function expects and returns.

2. **Editor support**: IDEs like VS Code use type hints to provide better autocompletion, error detection, and inline documentation.

3. **Error checking**: Tools like [mypy](https://mypy.readthedocs.io/) and [pyrefly](https://pyrefly.org/) analyze type hints to catch bugs *before* you run your code.

3. **Error checking**: Tools like [mypy](https://mypy.readthedocs.io/en/stable/) and [pyrefly](https://pyrefly.org/) analyze type hints to catch bugs *before* you run your code.

4. **LLM-generated code**: Large language models frequently produce code with type hints, so understanding the syntax helps you read and use their output.

### Type hints in scientific Python

Type hints connect to the {doc}`need for speed <need_for_speed>` discussion:

* High-performance libraries like [JAX](https://jax.readthedocs.io/) and [Numba](https://numba.pydata.org/) rely on knowing variable types to compile fast machine code.

* High-performance libraries like [JAX](https://docs.jax.dev/en/latest/) and [Numba](https://numba.pydata.org/) rely on knowing variable types to compile fast machine code.

* While these libraries infer types at runtime rather than reading Python type hints directly, the *concept* is the same --- explicit type information enables optimization.

* As the Python ecosystem evolves, the connection between type hints and performance tools is expected to grow.