On Jan 28, 2009, at 5:43 PM, Timmie wrote:
You could try: np.fromiter((_.hour for _ in dates_li), dtype=np.int) or np.array([_.hour for _ in dates_li], dtype=np.int)
I used dates_li only for the preparation of example data.
So let's suppose I have the array "dates_array" returned from a a function.
Just use dates_array instead of dates_li, then.
hours_array = dates_array.copy() for i in range(0, dates_array.size): hours_array[i] = dates_array[i].hour
* What's the point of making a copy of dates_array ? dates_array is a ndarray of object, right ? And you want to take the hours, so you should have an ndarray of integers for hours_array. * The issue I have with this part is that you have several calls to __getitem__ at each iteration. It might be faster to use create hours_array as a block: hours_array=np.array([_.hour for _ in dates_array], dtype=np.int)