+1 to this issue. I'm struggling big time with an 1800-year climate model dataset that I need to resample in order to make different annual means (June-May).

@lvankampenhout I agree that it would be nice if xarray had better support for PeriodIndexes.

Do you happen to be using a PeriodIndex because of pandas Timestamp-limitations? Despite the fact that generalized resample has not been implemented yet, I recommend you try using the new CFTimeIndex. As it turns out, for some one-off cases (like this one) resample is not too difficult to mimic using groupby. See the following example for your case. I'm assuming you're looking for resampling with the 'AS-JUN' anchored offset? ```python from itertools import product from cftime import DatetimeProlepticGregorian as datetime import numpy as np import xarray as xr

xr.set_options(enable_cftimeindex=True)

Set up some example data indexed by cftime.DatetimeProlepticGregorian objects

dates = [datetime(year, month, 1) for year, month in product(range(2, 5), range(1, 13))] da = xr.DataArray(np.arange(len(dates)), coords=[dates], dims=['time'])

Mimic resampling with the AS-JUN anchored offset

years = da.time.dt.year - (da.time.dt.month < 6) da['AS-JUN'] = xr.DataArray([datetime(year, 6, 1) for year in years], coords=da.time.coords) resampled = da.groupby('AS-JUN').mean('time').rename({'AS-JUN': 'time'}) This gives the following for `resampled`: <xarray.DataArray (time: 4)> array([ 2. , 10.5, 22.5, 32. ]) Coordinates: * time (time) object 0001-06-01 00:00:00 0002-06-01 00:00:00 ... This is analogous to using `resample(time='AS-JUN')` with a DataArray indexed by a DatetimeIndex:python import pandas as pd dates = pd.date_range('2002-01-01', freq='M', periods=36) da = xr.DataArray(np.arange(len(dates)), coords=[dates], dims='time') resampled = da.resample(time='AS-JUN').mean('time') which gives: <xarray.DataArray (time: 4)> array([ 2. , 10.5, 22.5, 32. ]) Coordinates: * time (time) datetime64[ns] 2001-06-01 2002-06-01 2003-06-01 2004-06-01 ```

@fmaussion just to clarify, #1252 is meant as an analogue to pandas' DatetimeIndex for non-standard calendars, and does not address resample (it would be nice to have at some point though). It is not intended to be used in place of (or provide similar functionality to) a PeriodIndex.

@MaximilianR perhaps it's also worth noting (as I understand it) xarray does not yet support upsampling with filling (see #563, and the docs). That being said, independent of that, there's definitely something odd going on, since attempting to downsample via a mean produces the same error: ``` In [21]: da = xr.DataArray(pd.Series(1, pd.period_range('2000-1', '2000-12', freq='W')).rename_axis('date'))

In [22]: da.resample('M', 'date', how='mean')

TypeError Traceback (most recent call last) <ipython-input-22-9c464b8e736c> in <module>() ----> 1 da.resample('M', 'date', how='mean')

/Users/spencerclark/xarray-dev/xarray/xarray/core/common.pyc in resample(self, freq, dim, how, skipna, closed, label, base, keep_attrs) 577 time_grouper = pd.TimeGrouper(freq=freq, how=how, closed=closed, 578 label=label, base=base) --> 579 gb = self.groupby_cls(self, group, grouper=time_grouper) 580 if isinstance(how, basestring): 581 f = getattr(gb, how)

/Users/spencerclark/xarray-dev/xarray/xarray/core/groupby.pyc in init(self, obj, group, squeeze, grouper, bins, cut_kwargs) 242 raise ValueError('index must be monotonic for resampling') 243 s = pd.Series(np.arange(index.size), index) --> 244 first_items = s.groupby(grouper).first() 245 if first_items.isnull().any(): 246 full_index = first_items.index

//anaconda/envs/xarray-dev/lib/python2.7/site-packages/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, kwargs) 3989 return groupby(self, by=by, axis=axis, level=level, as_index=as_index, 3990 sort=sort, group_keys=group_keys, squeeze=squeeze, -> 3991 kwargs) 3992 3993 def asfreq(self, freq, method=None, how=None, normalize=False):

//anaconda/envs/xarray-dev/lib/python2.7/site-packages/pandas/core/groupby.pyc in groupby(obj, by, kwds) 1509 raise TypeError('invalid type: %s' % type(obj)) 1510 -> 1511 return klass(obj, by, kwds) 1512 1513

//anaconda/envs/xarray-dev/lib/python2.7/site-packages/pandas/core/groupby.pyc in init(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, **kwargs) 368 level=level, 369 sort=sort, --> 370 mutated=self.mutated) 371 372 self.obj = obj

//anaconda/envs/xarray-dev/lib/python2.7/site-packages/pandas/core/groupby.pyc in _get_grouper(obj, key, axis, level, sort, mutated) 2390 # a passed-in Grouper, directly convert 2391 if isinstance(key, Grouper): -> 2392 binner, grouper, obj = key._get_grouper(obj) 2393 if key.key is None: 2394 return grouper, [], obj

//anaconda/envs/xarray-dev/lib/python2.7/site-packages/pandas/tseries/resample.pyc in _get_grouper(self, obj) 1059 def _get_grouper(self, obj): 1060 # create the resampler and return our binner -> 1061 r = self._get_resampler(obj) 1062 r._set_binner() 1063 return r.binner, r.grouper, r.obj

//anaconda/envs/xarray-dev/lib/python2.7/site-packages/pandas/tseries/resample.pyc in _get_resampler(self, obj, kind) 1055 raise TypeError("Only valid with DatetimeIndex, " 1056 "TimedeltaIndex or PeriodIndex, " -> 1057 "but got an instance of %r" % type(ax).name) 1058 1059 def _get_grouper(self, obj):

TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index' ```