True, i suppose there is no harm in accumulating with max precision, and storing the result in the Original dtype, unless otherwise specified, although i wonder if the current nditer supports such behavior. -----Original Message----- From: "Alan G Isaac" Sent: ‎24-‎7-‎2014 18:09 To: "Discussion of Numerical Python" Subject: Re: [Numpy-discussion] numpy.mean still broken for large float32arrays On 7/24/2014 5:59 AM, Eelco Hoogendoorn wrote to Thomas:

np.mean isn't broken; your understanding of floating point number is.

This comment seems to conflate separate issues: the desirable return type, and the computational algorithm. It is certainly possible to compute a mean of float32 doing reduction in float64 and still return a float32. There is nothing implicit in the name `mean` that says we have to just add everything up and divide by the count. My own view is that `mean` would behave enough better if computed as a running mean to justify the speed loss. Naturally similar issues arise for `var` and `std`, etc. See http://www.johndcook.com/standard_deviation.html for some discussion and references. Alan Isaac _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion