issue_comments: 445452721

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https://github.com/pydata/xarray/issues/2594#issuecomment-445452721 https://api.github.com/repos/pydata/xarray/issues/2594 445452721 MDEyOklzc3VlQ29tbWVudDQ0NTQ1MjcyMQ== 6628425 2018-12-08T11:36:16Z 2018-12-08T11:36:16Z MEMBER

It just takes the average.

However if you have an array of weights, it is straightforward to use groupby to take a weighted mean: ```python import numpy as np import pandas as pd import xarray as xr

times = pd.date_range('2001', periods=36, freq='MS') da = xr.DataArray(range(36), [('time', times)]) weights = times.shift(1, 'MS') - times weights = xr.DataArray(weights, [('time', times)]).astype('float')

annual_means = ((da * weights).groupby('time.year').sum('time') / weights.groupby('time.year').sum('time')) ```

Note this assumes that your DataArray does not contain missing values. If your array has missing values, you might want to use something like this: python annual_means = ((da * weights).groupby('time.year').sum('time') / weights.where(~np.isnan(da)).groupby('time.year').sum('time'))

 
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