Matthieu Brucher wrote: > Hi, > > I encountered cases where numpy.min() returned nan when the first and > the last values were nan. Didn't know of nanmin(), but I'll use them now ! Mmh, interesting. Indeed, a quick test shows that as long as the last value of a rank 1 array is not Nan, min ignore nan.
import numpy a = 0.1 * numpy.arange(10) numpy.min(a) a[:9] = numpy.nan numpy.min(a) # ignore Nan a = 0.1 * numpy.arange(10) a[-1] = numpy.nan numpy.min(a) # Does not ignore Nan cheers, David _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
