Well, there are lots of people out there who view "Pythonic" as a euphemism for awful tbh. Throughout my career I've seen multiple large Python codebases be rewritten in Java after they turned into a mess, including - perhaps most embarrassingly - Google's internal code review tool Mondrian, which was written by Guido van Rossum himself.

One reason Java codebases feel more abstract is that they're attempting to solve problems that the scripting worlds tend to just punt on, or handle in ways that don't scale well. And because Java's type system is nominal, solutions to those problems require names.

For example, a lot of the abstraction in Java codebases is to make things deeply testable. That's what drives dependency injection, which is where a lot of BeanFactory stuff comes from. What's the Python solution to that problem? Often they just hardly have tests by the standard of Java developers! This is why Python is big in ML research where codebases are often small disposable experiments, and why after a brief era where Django was popular it sort of died off in the wider server / big data space.

Another source of complexity and abstraction is a focus on pluggable ecosystems. Databases are abstracted by JDBC, and then again by an ORM, which then might be abstracted again by Micronaut or Spring, and at each layer there are multiple competing implementations available which implement standard APIs. This kind of thing happens much less in the scripting world. The downside is that scripting codebases are far more locked-in to their choice of libraries. Porting a Java codebase from one database or ORM to another is hugely easier, because the core APIs are all standardized.