[riazrizvi]

A benefit of Python is that it removes consideration of low level type details. You can just code your problem and think at a higher level, in terms of larger custom classes etc. This is great for developer speed. But to improve performance of code, you need to micro manage your types, you need to understand details about the compiler and the processor, you need to know what the machine is doing to the data to service your particular process, and streamline it accordingly.

Is there a magic function? If one crops up, it would get baked into Python, development of which is extremely active. So magic functions trend obsolete. The main thing developers can consistently do to improve performance, is micro manage types and algorithms to improve the performance of bottlenecks. This is done with a tool like Cython, but you have to learn the discipline of coding for performance, you have to learn the details of what the machine is doing with the data in your particular piece of code. When do you want to replace linked lists with arrays? When are you doing unnecessary type conversion? How overweight are your high frequency objects? How is memory being used? Are there more efficient ways on my microarchitecture to get this calculation done... Performance coding is a discipline like application coding.

Is this a problem with Python? I don't think so, because performance comes second in the developer process. Write the code first then optimize the real bottlenecks.