My perspective having developed with Python for over 20 years now: it depends on the nature and context of the problem being solved.

In my experience, you usually need a CPU intensive problem for the implementation language to be a speed concern. Many practical everyday problems solved with Python (outside of contests and specialized fields) tend to be I/O bound.

Secondly, even when you do have a CPU bound problem, many Python modules you might use to solve it are wrappers for C/C++ implementations.

As a result, I've come to think of Python as a control and integration language and less as a platform for base algorithm implementation.

Viewed that way, I've found Python to be unbeatable for speed of implementation and long-term maintainability (two of my top metrics for valuing code).