This is pretty good reading: Personal thoughts about Pyston’s outcome – kmod’s blog
"The thing I wish people understood about Python performance is that the difficulties come from Python’s extremely rich object model, not from anything about its dynamic scopes or dynamic types. The problem is that every operation in Python will typically have multiple points at which the user can override the behavior, and these features are used, often very extensively. Some examples are inspecting the locals of a frame after the frame has exited, mutating functions in-place, or even something as banal as overriding isinstance. "
Sure you can make Python fast in some cases where you use a small subset of the language. However, Python as a whole is extremely difficult to make fast because of the incredible freedom you have. The “compiler” can assume very little, so it is difficult to generate efficient code. Just consider the incredible amount of time and money that has been spent on trying to make it faster and the relatively mediocre success it has had (Pyston, Numba, PyPy etc).
This is also a good watch: Jeff Bezanson - Why is Julia fast? - YouTube