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https://github.com/pydata/xarray/pull/2222#issuecomment-396555289 https://api.github.com/repos/pydata/xarray/issues/2222 396555289 MDEyOklzc3VlQ29tbWVudDM5NjU1NTI4OQ== 6815844 2018-06-12T11:21:05Z 2018-06-12T11:21:05Z MEMBER

Do you have an example?

We raise a TypeError in the following example, because the dataset has datetime-type dimension.

```python In [3]: ds = xr.tutorial.load_dataset('air_temperature')

In [4]: ds Out[4]: <xarray.Dataset> Dimensions: (lat: 25, lon: 53, time: 2920) Coordinates: * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ... * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ... * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ... Data variables: air (time, lat, lon) float32 241.2 242.5 243.5 244.0 244.09999 ... Attributes: Conventions: COARDS title: 4x daily NMC reanalysis (1948) description: Data is from NMC initialized reanalysis\n(4x/day). These a... platform: Model references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

In [5]: ds.interp_like(ds.isel(lat=[0, 1, 2])) # -> TypeError ```

It might make sense to interpolate on numeric dimensions, and directly reindex on non-numeric dimensions.

I agree for the object type dimension, such as string, reindex is be appropriate and users might expect it. But for datetime dimension, I think users expect the interpolation rather than reindexing.

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