I have an array of datetime64 type:
dates = np.datetime64(['2010-10-17', '2011-05-13', "2012-01-15"])
Is there a better way than looping through each element just to get np.array of years:
years = f(dates)
#output:
array([2010, 2011, 2012], dtype=int8) #or dtype = string
I'm using stable numpy version 1.6.2.
I find the following tricks give between 2x and 4x speed increase versus the pandas method described in this answer (i.e. pd.DatetimeIndex(dates).year etc.). The speed of [dt.year for dt in dates.astype(object)] I find to be similar to the pandas method. Also these tricks can be applied directly to ndarrays of any shape (2D, 3D etc.)
dates = np.arange(np.datetime64('2000-01-01'), np.datetime64('2010-01-01'))
years = dates.astype('datetime64[Y]').astype(int) + 1970
months = dates.astype('datetime64[M]').astype(int) % 12 + 1
days = dates - dates.astype('datetime64[M]') + 1