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Whilst that is true, for data science related things I end up using Pandas (and Numpy) often which are fast.

So most of the heavy lifting is not done by python itself.

Yes, that's the case for most data scientists, and often for users in general. You can get pretty good performance levels from Python by delegating to C correctly. Even end users tend to do that because the data science stack of Python (numpy and the many libraries using it) spend almost all of their runtime in C routines.

If you are using Numpy correctly (even with little understanding of Python itself and its C-API) then virtually all your hot loops are executed in optimal C code.

So what? Pandas and numpy are still use with Python code using their APIs. The implementation details are not something the users care about. They write Python.

Not sure what you're arguing. If you use numpy and pandas properly, most of your computations happen in optimized C routines.

So you will write Python, but get very good resource (both time and space) efficiency.

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