Francesc, The datetime proposal is very impressive in its depth and thought. For me as well as many other people this would be a massive improvement to numpy and allow numpy to get a foothold in areas like econometrics where R/S is now dominant.
I had one question regarding casting of strings: I think it would be ideal if things like the following worked: >>> series = numpy.array(['1970-02-01','1970-09-01'], dtype = 'datetime64[D]') >>> series == '1970-02-01' [True, False] I view this as similar to: >>> series = numpy.array([1,2,3], dtype=float) >>> series == 2 [False,True,False] 1. However it does numpy recognizes that an int is comparable with a float and does the float cast. I think you want the same behavior between strings that parse into dates and date arrays. Some might object that the relationship between string and date is more tenuous than float and int, which is true, but having used my own homespun date array numpy extension for over a year, I've found that the first thing I did was wrap it into an object that handles these string->date translations elegantly and that made it infinately more usable from an ipython session. 2. Even more important to me, however, is the issue of date parsing. The mx library does many things badly but it does do a great job of parsing dates of many formats. When you parse '1/1/95' or 1995-01-01' it knows that you mean 19950101 which is really nice. I believe the scipy timeseries code for parsing dates is based on it. I would highly suggest starting with that level of functionality. The one major issue with it is an uninterpretable date doesn't throw an error but becomes whatever date is right now. That is obviously unfavorable. 3. Finally my current implementation uses floats uses nan to represent an invalid date. When you assign an element of an date array to None it uses nan as the value. When you assign a real date it puts in the equivalent floating point value. I have found this to be hugely beneficial and just wanted to float the idea of reserving a value to indicate the floating point equivalent of nan. People might prefer masked arrays as a solution, but I just wanted to float the idea. Forgive me if any of this has already been covered. There has been a lot of volume on this subject and I've tried to read it all diligently but may have missed a point or two. --Tom _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
