I won't touch Python either, but because I've been burned debugging large Python programs. Something that would have taken a minute in a statically typed language took hours of tracing data through the program to understand what was supposed to be in a dict. There are alternative languages that are pithy, statically typed, can write programs quickly, and can grow into large code bases that are maintainable; so there is never a reason to start a new project with Python today.
I've seen the same thing in .NET and Java where there's 800 layers of interface and impl and it's an adventure trying to find the actual business logic in all the layers of indirection
>so there is never a reason to start a new project with Python today
Nothing else has an ML/data ecosystem that compares. Perl/Go are maybe a distant 2nd
I deal with that all the time cause an adjacent team uses Java with tons of boilerplate and frameworks. At that point, your static typing isn't so static. Takes them forever to make changes, to the point where I started taking over responsibilities using Python code.
Most python written at a large scale uses types (TypedDict) and/or Pydantic for safety and never plain dict objects. That's a code smell in any language, we can stuff data into `map[string]interface{}` all day long and cause problems downstream.
