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  • When use groupby to convert monthly array into yearly array by mean (dim=time), does xr consider the weights of each month or just take average? · 1 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
445452721 https://github.com/pydata/xarray/issues/2594#issuecomment-445452721 https://api.github.com/repos/pydata/xarray/issues/2594 MDEyOklzc3VlQ29tbWVudDQ0NTQ1MjcyMQ== spencerkclark 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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   When use groupby to convert monthly array into yearly array by mean (dim=time), does xr consider the weights of each month or just take average? 388901771

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