issue_comments: 449533135

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https://github.com/pydata/xarray/pull/2593#issuecomment-449533135 https://api.github.com/repos/pydata/xarray/issues/2593 449533135 MDEyOklzc3VlQ29tbWVudDQ0OTUzMzEzNQ== 6628425 2018-12-22T01:04:32Z 2018-12-22T01:04:32Z MEMBER

Yes, I noticed this too. I think it's related to changes made here: https://github.com/pandas-dev/pandas/pull/24347. At least in the test cases that I've run, I've only seen it make a difference in the NaN placement at the end of the time series.

For example with pandas 0.23.4: ``` In [1]: import xarray as xr; import pandas as pd

In [2]: nptimes = pd.date_range('2000', periods=2000)

In [3]: nptime_da = xr.DataArray(range(2000), [('time', nptimes)])

In [4]: nptime_da.resample(time='4AS').mean('time') Out[4]: <xarray.DataArray (time: 3)> array([ 730., 1730., nan]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2004-01-01 2008-01-01 and with pandas master: In [4]: nptime_da.resample(time='4AS').mean('time') Out[4]: <xarray.DataArray (time: 2)> array([ 730., 1730.]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2004-01-01 `` I feel like the result under pandas master makes more sense, given the input array, whose final date is 2005-06-22. Adding an additional bin labeled 2008-01-01 to the resampled time series seems superfluous given that with the defaultlabel='left'` it's clear no data from the original time series would fall in that bin.

 
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