Diez B. Roggisch wrote: > I'm pretty sure you're out of luck here - even _if_ NumPy would handle > arbitrary data-types (AFAIK it doesn't, but then I'm not a total expert > there), it certainly won't be able to make its hi-performance functions > work on them.
Yes, one can make numpy arrays with "object" as its type. One can even extend the C-level parts as well. For example, we have an experimental package in the scipy sandbox for uniform time series that uses mx.DateTime. http://www.scipy.org/TimeSeriesPackage > What you could do would be to convert the date-column into a timestamp, > which is a int/long, and use that. Would that help? This is frequently what I do. For dates, I like Modified Julian Day Numbers although I am sure that would horrify some people more knowledgeable than I. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
