id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
778123296,MDExOlB1bGxSZXF1ZXN0NTQ4MjY4ODg5,4762,Print number of variables in repr,14371165,closed,0,,,6,2021-01-04T14:13:17Z,2021-05-18T18:17:50Z,2021-01-12T00:21:19Z,MEMBER,,0,pydata/xarray/pulls/4762,"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
```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4762/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
778083748,MDU6SXNzdWU3NzgwODM3NDg=,4761,Dataset.interp drops boolean variables,14371165,closed,0,,,0,2021-01-04T13:09:56Z,2021-05-13T15:28:15Z,2021-05-13T15:28:15Z,MEMBER,,,,"<!-- Please include a self-contained copy-pastable example that generates the issue if possible.

Please be concise with code posted. See guidelines below on how to provide a good bug report:

- Craft Minimal Bug Reports: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples: https://stackoverflow.com/help/mcve

Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->

**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_like `use `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**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

xr.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.8.5 (default, Sep  3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)]
python-bits: 64
OS: Windows
libhdf5: 1.10.4
libnetcdf: None

xarray: 0.16.2
pandas: 1.1.5
numpy: 1.17.5
scipy: 1.4.1
netCDF4: None
pydap: None
h5netcdf: None
h5py: 2.10.0
Nio: None
zarr: None
cftime: None
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2020.12.0
distributed: 2020.12.0
matplotlib: 3.3.2
cartopy: None
seaborn: 0.11.1
numbagg: None
pint: None
setuptools: 51.0.0.post20201207
pip: 20.3.3
conda: 4.9.2
pytest: 6.2.1
IPython: 7.19.0
sphinx: 3.4.0


</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4761/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue