What are the limitations of type annotations in Python?

What are the limitations of type annotations in Python?

Type annotations in Python enhance code readability and help with static type checking, but they have limitations. They are optional, which means not all code will have them, and they don't enforce type checking at runtime. Additionally, complex types can become cumbersome and may lead to reduced readability.

Can you give examples of where type annotations might be misleading?

Sure! One common issue is when using Any type, which can lead to unexpected behavior since it bypasses type checking. Another example is when a function's return type is annotated as a specific type, but it can actually return different types based on conditions, leading to confusion.

How do type annotations affect performance?

Type annotations themselves do not impact runtime performance since they are not enforced during execution. However, they can improve development speed and reduce bugs, which indirectly enhances overall performance by leading to more efficient code.

Are there any tools to help with type checking in Python?

Yes, tools like mypy and pyright are popular for static type checking in Python. They analyze your code and report type inconsistencies based on the annotations you've provided.