A lot of these problem spaces can get away with single threaded performance because maybe they're generating a report or running an analysis once a day or at even slower frequency. I work in a field where numerical correctness and readability is important for prototyping control algorithms (I work on advanced sensors) and python satisfies for those properties for our analysis and prototyping work.
When we really want or need performance we rewrite the slow part in C++ and use pybind to call into it. For all the real implementations that run in a soft real time system, everything is done in C++ or C depending on the ecosystem.
Because you say it doesn't make it true. It's not that painful or painful at all really. Good abstractions and planning make writing and maintaining a python easy, just like in any language.
When we really want or need performance we rewrite the slow part in C++ and use pybind to call into it. For all the real implementations that run in a soft real time system, everything is done in C++ or C depending on the ecosystem.