On 16 March 2015 at 15:53, Dave Hirschfeld <dave.hirschf...@gmail.com> wrote: > I have a number of large arrays for which I want to compute the mean and > standard deviation over a particular axis - e.g. I want to compute the > statistics for axis=1 as if the other axes were combined so that in the > example below I get two values back > > In [1]: a = randn(30, 2, 10000) > ... > Both methods are however significantly slower than the initial attempt: > > In [9]: %timeit a.mean(0).mean(-1) > 1000 loops, best of 3: 1.2 ms per loop > > Perhaps because it allocates a smaller temporary? > > For those who like a challenge: is there a faster way to achieve what > I'm after?
You'll probably find it faster if you swap the means around to make an even smaller temporary: a.mean(-1).mean(0) Oscar _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
