[throwaway314155]

Language abstractions that are not "zero-cost" inevitably lead to worse performance. Python has many such abstractions designed to improve developer experience. I think that's all the person you're responding to meant.

[pjmlp]

Which is proven wrong by optimizing Common Lisp compilers wiht similar abstractions.

Or the SELF workstation environment at Sun, whose research ideas in optmizing JIT compilers eventually landed on V8.

[bdbenton5255]

Yes, thank you.

[vlovich123]

It’s not mainly the syntax though although it has a marginal effect. It’s partially the lack of typing information (which is syntax) but mostly that it runs interpreted. Pypy is significantly faster because it applies JIT to generate machine code directly to represent the Python code and it’s significantly faster in most cases. Another huge cost is in multi-threaded scenarios where Python has the GIL (even in single threaded there’s a cost) which is an architectural and not syntactic decision.

For example, Python has a drastically simpler syntax in some ways than C++ (ignoring the borrow annotations). In many ways it can look like Python code. Yet its performance is the same as c++ because it’s AOT compiled and has an explicit type system to support that.

TLDR: most of python’s slowness is not syntactic but architectural design decisions about how to run the code which is why alternate Python implementations (IronPython, PyPy) typically run faster.