BUG: Resample on PeriodIndex not working?

import xarray as xr
import pandas as pd
da = xr.DataArray(pd.Series(1, pd.period_range('2000-1', '2000-12', freq='W')).rename_axis('date'))

da.resample('B', 'date', 'ffill')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-eb64a66a8d1f> in <module>()
      3 da = xr.DataArray(pd.Series(1, pd.period_range('2000-1', '2000-12', freq='W')).rename_axis('date'))
      4
----> 5 da.resample('B', 'date', 'ffill')

/Users/maximilian/drive/workspace/xarray/xarray/core/common.py 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/maximilian/drive/workspace/xarray/xarray/core/groupby.py 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

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/generic.py 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):

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/groupby.py in groupby(obj, by, **kwds)
   1509         raise TypeError('invalid type: %s' % type(obj))
   1510
-> 1511     return klass(obj, by, **kwds)
   1512
   1513

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/groupby.py 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

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/groupby.py 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

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py 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

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/tseries/resample.py 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'

[spencerkclark]

+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?

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:

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

[stale[bot]]

In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the stale label; otherwise it will be marked as closed automatically