issue_comments: 506747197

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https://github.com/pydata/xarray/issues/3053#issuecomment-506747197 https://api.github.com/repos/pydata/xarray/issues/3053 506747197 MDEyOklzc3VlQ29tbWVudDUwNjc0NzE5Nw== 6628425 2019-06-28T14:07:40Z 2019-06-28T14:07:40Z MEMBER

Sure thing -- that's a valid concern. Perhaps it makes sense then to retain the MultiIndex that gets created through stacking: stacked = ds.stack(time=('initialisation_date', 'forecast_horizon')) Then you can select via the MultiIndex levels, e.g. ```

stacked.sel(forecast_horizon=np.timedelta64(28, 'D')) <xarray.Dataset> Dimensions: (initialisation_date: 12, lat: 36, lon: 45, number: 51) Coordinates: * lon (lon) float64 33.5 33.7 33.9 34.1 ... 41.85 42.05 42.25 * lat (lat) float64 -5.175 -5.176 -5.177 ... -5.201 -5.202 * number (number) int64 0 1 2 3 4 5 6 7 ... 44 45 46 47 48 49 50 valid_time (initialisation_date) datetime64[ns] 2018-02-28 ... 2019-01-28 * initialisation_date (initialisation_date) datetime64[ns] 2018-01-31 ... 2018-12-31 Data variables: precip (number, lat, lon, initialisation_date) float64 0.4954 ... 0.5873 Or, if you prefer to select using the `'valid_time'` coordinate, you can do: stacked.swap_dims({'time': 'valid_time'}).sel(valid_time='2018-04') <xarray.Dataset> Dimensions: (lat: 36, lon: 45, number: 51, valid_time: 7) Coordinates: * lon (lon) float64 33.5 33.7 33.9 34.1 ... 41.65 41.85 42.05 42.25 * lat (lat) float64 -5.175 -5.176 -5.177 -5.177 ... -5.2 -5.201 -5.202 * number (number) int64 0 1 2 3 4 5 6 7 8 ... 42 43 44 45 46 47 48 49 50 * valid_time (valid_time) datetime64[ns] 2018-04-02 2018-04-03 ... 2018-04-30 time (valid_time) object (Timestamp('2018-01-31 00:00:00', freq='M'), Timedelta('61 days 00:00:00')) ... (Timestamp('2018-03-31 00:00:00', freq='M'), Timedelta('30 days 00:00:00')) Data variables: precip (number, lat, lon, valid_time) float64 -1.084 0.8517 ... 1.784 ```

 
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