Show the printed and total number of variables in the repr.

• [x] Passes isort . && black . && mypy . && flake8

```python import numpy as np import xarray as xr

a = np.arange(0, 15) b = np.core.defchararray.add("long_variable_name", a.astype(str)) c = np.arange(0, 25) d = np.core.defchararray.add("attr_", c.astype(str)) e = {k: 2 for k in d} coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=np.array([0, 1]).astype(bool), dims=["time"], coords=coords, ) ds1 = xr.Dataset(data_vars) ds1.attrs = e

The repr now shows how many attributes in total there are:

print(ds1) Out[10]: <xarray.Dataset> Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: (12/15) long_variable_name0 (time) bool False True long_variable_name1 (time) bool False True long_variable_name2 (time) bool False True long_variable_name3 (time) bool False True long_variable_name4 (time) bool False True long_variable_name5 (time) bool False True ... long_variable_name9 (time) bool False True long_variable_name10 (time) bool False True long_variable_name11 (time) bool False True long_variable_name12 (time) bool False True long_variable_name13 (time) bool False True long_variable_name14 (time) bool False True Attributes: (12/25) attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_19: 2 attr_20: 2 attr_21: 2 attr_22: 2 attr_23: 2 attr_24: 2 ```

What happened: Dataset.interp silently drops boolean variables.

What you expected to happen: If I'm interpolating a group of variables I expect to get all of them back in the correct shape with relevant values in them.

If the variables are boolean or object arrays I don't expect it to do linear interpolation because it doesn't make sense but stepwise interpolation like nearest or zero order interpolation should be fine to expect.

Minimal Complete Verifiable Example:

```python import numpy as np a = np.arange(0, 5) b = np.core.defchararray.add("long_variable_name", a.astype(str)) coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=np.array([0, 1]).astype(bool), dims=["time"], coords=coords, ) ds1 = xr.Dataset(data_vars)

Print raw data:

print(ds1) Out[3]: <xarray.Dataset> Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: long_variable_name0 (time) bool False True long_variable_name1 (time) bool False True long_variable_name2 (time) bool False True long_variable_name3 (time) bool False True long_variable_name4 (time) bool False True

Interpolate:

ds1 = ds1.interp( time=da.array([0, 0.5, 1, 2]), assume_sorted=True, method="nearest", kwargs=dict(fill_value="extrapolate"), )

Print interpolated data:

<xarray.Dataset> Dimensions: (time: 4) Coordinates: * time (time) float64 0.0 0.5 1.0 2.0 Data variables: empty ```

Anything else we need to know?: ds.interp_likeuse ds.reindex in these cases which seems like a good choice in ds.interp as well. But I think that both ds.interp and ds.interp_like should fill by default with nearest value instead of np.nan because we're still requesting interpolation.

Environment: