There are some extremely CPU-intensive low-level operations that you can easily write in C and expose as a Python API, like what Numpy and Pandas do. You can then write really efficient algorithms in pure Python. As long as those low-level operations are fast, those Python-only algorithms will also be fast.

I don't think this is necessarily "cheating" or "just calling disguised C functions." As an example, you can write an efficient linear regression algorithm with Numpy, even though there's nothing in Numpy that supports linear regression specifically, it's just one of the ways a Python programmer can arrange Numpy's low-level primitives. If you invent some new numerical algorithm to solve some esoteric problem in chemistry, you may be able to implement it efficiently in Python too, even if you're literally the first person ever writing it in any language.

The actual problem is that it's hard for people to get an intuition of which Python operations can be made fast and which can't, AST and file manipulation are sadly in the latter group.