Implement max/mean pooling(with stride) with numpy

I wanted to know how to implement a simple max/mean pooling with numpy. I was reading Max and mean pooling with numpy, but unfortunately it assumed the stride was the same as the kernel size. Is there a numpythonic way to do this? Also it would be nice if this were to work for any dimension, but of course not neccesary.