issue_comments: 143116745
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/501#issuecomment-143116745 | https://api.github.com/repos/pydata/xarray/issues/501 | 143116745 | MDEyOklzc3VlQ29tbWVudDE0MzExNjc0NQ== | 12929592 | 2015-09-25T03:49:53Z | 2015-09-25T03:49:53Z | NONE | Thank you, but can we use the mask and apply it to another xray dataset - so you only take the values from one dataset that fall in the region of the mask)? I have tried below (but this doesn't work). Thanks ds.states.where(ds.states == state_ids['California']).plot() dstemp=xray.open_mfdataset(filepath) ds_variable=dstemp['temp'] monthlymean=ds_variable.resample('1MS', dim='time', how='mean') meanmonthlycaliforniatemp=ds.states.where(ds.states==state_ids['California']).monthlymean.groupby('time').mean() meanmonthlycaliforniatemp.to_pandas().plot() |
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