-----Original Message----- ...snip what I want is minimum.reduce(a,1), except that the zeros would be ignored. At first glace, Masked arrays seemed the perfect option (as that's what the zero means, no real data), but there doesn't seem to be a minimum.reduce() in MA. --> I'll add this to my list. Meanwhile you could do something like this, where x is your array: y = MA.masked_equal(x,0,copy=0) Numeric.minimum.reduce(y.filled(100000), 1) If x is floating point use masked_value rather than masked_equal. --- By the way, what is the downside to Masked Arrays? they seem to be a really powerful option. Is there any performance hit to using a masked array over a regular onw if there is no mask? If not, would it make sense to move toward just using Masked arrays for everything? thanks, -Chris --> There are two problems. There is a hit, even in the case of no mask. It is hard to quantify because it depends on what operations you do. Much of that could be eliminated if MA was in C but it is Python. It would have driven me mad to write it in C, however. More seriously, there are a few semantic issues where it isn't clear what you mean if a mask is present.