Masked array seems definitely to be the way to go, thanks a lot. I must say that this entire issue doesn't make much sense to me: my understanding is the a NaN is different from an INF, therefore one would assume that really there is no reason why a not-number should not be ignored by default by all the array manipulating functions. On 6/25/07, Pierre GM <pgmdevlist@gmail.com> wrote:
On Monday 25 June 2007 14:15:20 Giorgio F. Gilestro wrote:
Thanks. Actually those I care the most are average and std. Is there a way to know the number of NaN in an array?
Giorgio, You could use: numpy.isnan(x).sum()
But once again <push_product> masked arrays were designed to handle this kind of situation seamlessly. Just create a masked_array masked_array(x, mask=isnan(x)) and use the regular functions/methods on the masked array. </push_product> _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion