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
946439792,MDU6SXNzdWU5NDY0Mzk3OTI=,5614,col_wrap arg to da.plot() can be a string?,7461221,open,0,,,2,2021-07-16T16:31:24Z,2021-07-16T18:12:41Z,,NONE,,,,"**What happened**:
 if you try to plot a data array with 3 dimensions but the third dim has length 1, then `col_wrap` accepts a dim name and plots facets with `pcolormesh`. according to the documentation `col_wrap` argument should be an `int` so I assume this is unintentional http://xarray.pydata.org/en/stable/generated/xarray.DataArray.plot.html

if dim 3 has length >1, then `col_wrap` still takes a `str` but uses `hist()`


**What you expected to happen**:
I expected an error if `col_wrap` is not an `int`.  I also expected da.plot(col='3rd_dim') to give me one facet if the dim has length 1 - this (mis)behavior is described in [#620](https://github.com/pydata/xarray/issues/620)

all a bit confusing :). I thought I understood plotting but then ran into bug #620 and started fumbling around. I am trying to plot a dataarray in a function but I don't know ahead of time how many values 3rd dim has.  I also wanted to avoid limiting my function to `pcolormesh()` only. `da.plot.pcolormesh(col='3rd_dim')` works to plot the images regardless of dim length.

**Minimal Complete Verifiable Example**:
```
da_list = []
for i in np.arange(1,3):
    data = np.ones((64, 48), dtype = 'uint8') * (200-i*15)
    oneframe = xr.DataArray(
        data=data[..., None],
        dims=('x', 'y','measurement'),
        coords={'measurement':[f't{i*40}']}
    )

    da_list.append(oneframe)

twoframes = xr.concat(da_list, 'measurement')
```

```
oneframe.plot(col_wrap='measurement') # Oddly, facets with pcolormesh()
twoframes.plot(col_wrap='measurement') # Oddly, a single facet with hist()
oneframe.plot(col='measurement') # ValueError: IndexVariable objects must be 1-dimensional ala issue #620
twoframes.plot(col='measurement') # facets with pcolormesh()
oneframe.plot.pcolormesh(col='measurement') # facets with pcolormesh()
twoframes.plot.pcolormesh(col='measurement') #facets with pcolormesh()
```

**Anything else we need to know?**:
@nfahlgren noted a workaround using FacetGrid directly

```
from xarray.plot.facetgrid import FacetGrid
gd = FacetGrid(data=oneframe, col=""measurement"")
gd.map_dataarray(xr.plot.imshow, ""x"", ""y"")
```
is consistent with:
```
gd = FacetGrid(data=twoframes, col=""measurement"")
gd.map_dataarray(xr.plot.imshow, ""x"", ""y"")
```

**Environment**:
Windows 10, python 3.7

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

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.10 | packaged by conda-forge | (default, Feb 19 2021, 15:37:01) [MSC v.1916 64 bit (AMD64)]
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
libhdf5: None
libnetcdf: None

xarray: 0.17.0
pandas: 1.2.3
numpy: 1.20.1
scipy: 1.6.0
netCDF4: None
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: 2.7.0
cftime: None
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2021.02.0
distributed: 2021.02.0
matplotlib: 3.3.4
cartopy: None
seaborn: None
numbagg: None
pint: None
setuptools: 49.6.0.post20210108
pip: 21.1.2
conda: 4.10.1
pytest: 6.2.2
IPython: 7.24.1
sphinx: None

</details>
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