Looking to improve quality of a fairly large Python project. I am happy with the types of warnings PyLint gives me. However, they are just too numerous and hard to enforce across a large organization. Also I believe that some code is more critical/sensitive than others with respect to where the next bug may come. For example I would like to spend more time validating a library method that is used by 100 modules rather than a script that was last touched 2 years ago and may not be used in production. Also it would be interesting to know modules that are frequently updated.
Is anyone familiar with tools for Python or otherwise that help with this type of analysis?
I'm afraid you are mostly on your own.
If you have decent set of tests, look at code coverage and dead code.
If you have a decent profiling setup, use that to get a glimpse of what's used more.
In the end, it seems you are more interested in fan-in/fan-out analysis, I'm not aware of any good tools for Python, primarily because static analysis is horribly unreliable against a dynamic language, and so far I didn't see any statistical analysis tools.
I reckon that this information is sort of available in JIT compilers -- whatever (function, argument types) is in cache (compiled) those are used the most. Whether or not you can get this data out of e.g. PyPy I really don't have a clue.
Identifying "sensitive" code in your application