I see people conflate strong/weak and static/dynamic quite often. Python is strong[1]/dynamic, with optional static typing through annotations and a type checker (mypy, pyright, etc).
Perhaps the easiest way to add static types to data is with pydantic. Here's an example of using pydantic to type-check data provided via an external yaml configuration file:
[1] strong/weak are not strictly defined, as compared to dynamic/static, but Python is absolutely on the strong end of the scale. You'll get a runtime TypeError if you try to add a number to a string, for example, compared to say JavaScript which will happily provide a typically meaningless "wat?"-style result.
I see people conflate strong/weak and static/dynamic quite often. Python is strong[1]/dynamic, with optional static typing through annotations and a type checker (mypy, pyright, etc).
Perhaps the easiest way to add static types to data is with pydantic. Here's an example of using pydantic to type-check data provided via an external yaml configuration file:
https://news.ycombinator.com/item?id=41508243
[1] strong/weak are not strictly defined, as compared to dynamic/static, but Python is absolutely on the strong end of the scale. You'll get a runtime TypeError if you try to add a number to a string, for example, compared to say JavaScript which will happily provide a typically meaningless "wat?"-style result.