[jhokanson]

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."

[masklinn]

Why would Python provide a mergesort option when timsort was the replacement for a standard mergesort?

`heapq.merge` is not a mergesort, it's a merge of sorted sequences (aka just the merge part of a mergesort).

[TheRealPomax]

Turns out that at least in terms of performance, everyone using numpy is fine with this. It just needs to run fast enough, not as fast as possible ;)

[tehjoker]

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.

[CogitoCogito]

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.

[toxik]

Tangential but np.bincount is typically the fast version of np.unique. Not entirely the same thing, but it’s worth knowing about it.

[kzrdude]

mergesort mandates that it's a stable sort, so I guess they get away with replacing "like for like" that way. For varying definitions of like.