Python type annotations are a good source of lightweight, easy to maintain metadata for many frameworks that provide valuable automatic handling of specific dat

Python type annotations are a good source of lightweight, easy to maintain metadata for many frameworks that provide valuable automatic handling of specific data types (for example defining deserialization and deserialization of nice class types with field declarations, like attrs/cattrs or Pydantic, or defining command line interfaces with function parameter declarations, like Appeal). Expecting type annotations to be more generally useful is mostly projected, baseless declaration anxiety: tools and methods that are useful in other languages are expected to be relevant in Python, certainly comforting for the type-addicted programmer but not necessarily useful. Declaring types is perceived as normal, as a necessary burden and dynamic typing is perceived as missing important structural elements. Consider the error pattern of accessing a value as if it had a different type: in C or C++ consequences are dire (possibly undetected and cascading memory corruption), likelihood is very high due to specific language features (pointers and references, raw arrays, casts, weak typing in general) and detailed type declarations are a useful mitigation because they turn run time catastrophes into actionable compile time reports, while in Python, thanks to robust duck typing without dangerous complications, consequences are mild (reasonable error messages or wrong results, before corrupting memory), likelihood is low (specific instances of unexpected and malformed data or gross API misunderstandings) and type declarations do nothing.