Aronne Merrelli wrote: > > I can recreate this error if tab is a structured ndarray - what is the > dtype of tab? > > If that is correct, I think you could fix this by simplifying things. > Since > tab is already an ndarray, you should not need to convert it back into a > python list. By converting the ndarray back to a list you are making an > extra level of "wrapping" as a python object, which is ultimately why you > get that error about adding numpy.void. > > Unfortunately you cannot take directly take a mean of a struct dtype; > structs are generic so they could have fields with strings, or objects, > etc, that would be invalid for a mean calculation. However the following > code fragment should work pretty efficiently. It will make a 1-element > array of the same dtype as tab, and then populate it with the mean value > of > all elements where the length is >= 15. Note that dtype.fields.keys() > gives > you a nice way to iterate over the fields in the struct dtype: > > length_mask = tab['length'] >= 15 > tab_means = np.zeros(1, dtype=tab.dtype) > for k in tab.dtype.fields.keys(): > tab_means[k] = np.mean( tab[k][mask] ) > > In general this would not work if tab has a field that is not a simple > numeric type, such as a str, object, ... But it looks like your arrays are > all numeric from your example above. > > Hope that helps, > Aronne > HI Aronne, Thanks for your replay. Indeed, tab is a mix of different column types: tab.dtype: [('sgi', '<i8'), ('length', '<i8'), ('nident', '<i8'), ('pident', '<f8'), ('positive', '<i8'), ('ppos', '<f8'), ('mismatch', '<i8'), ('qstart', '<i8'), ('qend', '<i8'), ('sstart', '<i8'), ('send', '<i8'), ('gapopen', '<i8'), ('gaps', '<i8'), ('evalue', '<f8'), ('bitscore', '<f8'), ('score', '<f8')] Interestingly, I couldn't be able to import some columns of digits as strings like as with R dataframe objects. I'll try to adapt your example to my needs and let you know the results. Regards. -- View this message in context: http://old.nabble.com/numpy.mean-problems-tp32945124p32955052.html Sent from the Numpy-discussion mailing list archive at Nabble.com.
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