As someone that rarely works with Python lists, as opposed to numpy arrays, I was pleasantly surprised to see numpy does what I would expect in providing a mergesort option. I'm surprised Python doesn't, other than via heapq and only implemented in Python (according to my reading of the post and a very quick Google search).
Oops, just for fun the numpy documentation currently states: "The datatype determines which of ‘mergesort’ or ‘timsort’ is actually used, even if ‘mergesort’ is specified. User selection at a finer scale is not currently available." Awesome ...
Also, apparently mergesort may also be done using radix sort for integers "‘mergesort’ and ‘stable’ are mapped to radix sort for integer data types."
This is not true. I have written many libraries to improve on the performance of numpy for image processing applications. Sort backs many operations, such as unique, that result in painfully slow code.
I too have written a lot of extension code to speed up numpy code. It's often not even especially difficult since any code at that level tends to be very algorithmic and looks essentially the same if written in numpy or C++. Having control of the memory layout and algorithms can be a huge win.
Of course I don't _usually_ do this, but that's just be most of the code I write doesn't need it. But it's not at all some crazy sort of thing to do.
Oops, just for fun the numpy documentation currently states: "The datatype determines which of ‘mergesort’ or ‘timsort’ is actually used, even if ‘mergesort’ is specified. User selection at a finer scale is not currently available." Awesome ...
Also, apparently mergesort may also be done using radix sort for integers "‘mergesort’ and ‘stable’ are mapped to radix sort for integer data types."