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.
As datetime is not stable in numpy I would use pandas for this:
In [52]: import pandas as pd In [53]: dates = pd.DatetimeIndex(['2010-10-17', '2011-05-13', "2012-01-15"]) In [54]: dates.year Out[54]: array([2010, 2011, 2012], dtype=int32) Pandas uses numpy datetime internally, but seems to avoid the shortages, that numpy has up to now.
I find the following tricks give between 2x and 4x speed increase versus the pandas method described above (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 There should be an easier way to do this, but, depending on what you're trying to do, the best route might be to convert to a regular Python datetime object:
datetime64Obj = np.datetime64('2002-07-04T02:55:41-0700') print datetime64Obj.astype(object).year # 2002 print datetime64Obj.astype(object).day # 4 Based on comments below, this seems to only work in Python 2.7.x not Python 3.x
If you upgrade to numpy 1.7 (where datetime is still labled as experimental) the following should work.
dates/np.timedelta64(1,'Y') There's no direct way to do it yet, unfortunately, but there are a couple indirect ways:
[dt.year for dt in dates.astype(object)] or
[datetime.datetime.strptime(repr(d), "%Y-%m-%d %H:%M:%S").year for d in dates] both inspired by the examples here.
Both of these work for me on Numpy 1.6.1. You may need to be a bit more careful with the second one, since the repr() for the datetime64 might have a fraction part after a decimal point.
Using numpy version 1.10.4 and pandas version 0.17.1,
dates = np.array(['2010-10-17', '2011-05-13', '2012-01-15'], dtype=np.datetime64) pd.to_datetime(dates).year I get what you're looking for:
array([2010, 2011, 2012], dtype=int32) Anon's answer works great for me, but I just need to modify the statement for days
from:
days = dates - dates.astype('datetime64[M]') + 1to:
days = dates.astype('datetime64[D]') - dates.astype('datetime64[M]') + 1Another possibility is:
np.datetime64(dates,'Y') - returns - numpy.datetime64('2010') or
np.datetime64(dates,'Y').astype(int)+1970 - returns - 2010 but works only on scalar values, won't take array