issue_comments: 218403213

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https://github.com/pydata/xarray/issues/422#issuecomment-218403213 https://api.github.com/repos/pydata/xarray/issues/422 218403213 MDEyOklzc3VlQ29tbWVudDIxODQwMzIxMw== 10194086 2016-05-11T09:06:49Z 2016-05-11T09:07:24Z MEMBER

Sounds like a clean solution. Then we can defer handling of NaN in the weights to weighted (e.g. by a skipna_weights argument in weighted). Also returning sum_of_weights can be a method of the class.

We may still end up implementing all required methods separately in weighted. For mean we do:

(data * weights / sum_of_weights).sum(dim=dim)

i.e. we use sum and not mean. We could rewrite this to:

(data * weights / sum_of_weights).mean(dim=dim) * weights.count(dim=dim)

However, I think this can not be generalized to a reduce function. See e.g. for std http://stackoverflow.com/questions/30383270/how-do-i-calculate-the-standard-deviation-between-weighted-measurements

Additionally, weighted does not make sense for many operations (I would say) e.g.: min, max, count, ...

 
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