I ran into this early in my career when a senior engineer at a startup reviewed my Python PR for operating on a CSV dataset and insisted I rewrite it in Pandas for performance. It was a very simple program with naive Python (a handful of lines), but the Pandas version was far longer and more complex (I had to have the senior engineer help because it took some advanced Pandas-fu, and he spent nearly a full day on it), and it ultimately ended up being an order of magnitude slower because we ultimately had to call back into Python for each cell.

I actually don’t think “premature optimization” is all that bad in the general case, but you have to be educated about it (and I’ve met a lot of people in Python shops who think that Pandas or multiprocessing will cure all performance ails), and you should specifically think about how costly is a given optimization going to be to maintain or back out of if you’re wrong about the performance benefits.

In general, I’ve never worked on a Python project where we didn’t have to do weird, inordinately expensive things to work around performance issues (though I’ve never worked on a rudimentary CRUD app either) nor has “just throw Pandas/C/multiprocessing at it” ever adequately addressed our most significant performance bottlenecks (usually the solution looks something like Spark, all to do something that would have been sufficiently performant with naive Java or Go). This might just be my experience working on nontrivial SaaS apps; maybe if you’re just doing straight data science or CRUD apps or workloads that aren’t latency-sensitive (mind you, we struggled to keep per-request performance in the tens of seconds, so I use “latency-sensitive” very loosely), Python/Pandas will be just fine. We also ran into other problems with Python, such as packaging and distribution; notably our lambda functions were routinely too large because the pandas branch of the dependency tree was itself more than half of the permitted artifact size (to work around, we switched to Fargate tasks, which have a much larger size limit but take 30s or minutes to boot up).