Maybe you are right, but by providing many resolutions we are trying to cope with the needs of people that are using them a lot. In particular, we are willing that the authors of the timseries scikit can find on these new dtype a fair replacement of their Date class (our proposal will be not so featured, but...).
I think a basic date/time dtype for numpy would be a nice addition for general usage. Now as for the timeseries module using this dtype for most of the date-fu that goes on... that would be a bit more challenging. Unless all of the frequencies/resolutions currently supported in the timeseries scikit are supported with the new dtype, it is unlikely we would be able to replace our implementation. In particular, business day frequency (Monday - Friday) is of central importance for working with financial time series (which was my motivation for the original prototype of the module). But using plain integers for the DateArray class actually seems to work pretty well and I'm not sure a whole lot would be gained by using a date dtype. That being said, if someone creates a fork of the timeseries module using a new date dtype at it's core and it works amazingly well, then I'd probably get on board. I just think that may be difficult to do with a general purpose date dtype suitable for inclusion in the numpy core. - Matt