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
775322346,MDU6SXNzdWU3NzUzMjIzNDY=,4736,Limit number of data variables shown in repr,14371165,closed,0,,,2,2020-12-28T10:15:26Z,2021-01-04T02:13:52Z,2021-01-04T02:13:52Z,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**:
xarray feels very unresponsive when using datasets with >2000 data variables because it has to print all the 2000 variables everytime you print something to console.

**What you expected to happen**:
xarray should limit the number of variables printed to console. Maximum maybe 25?
Same idea probably apply to dimensions, coordinates and attributes as well,

pandas only shows 2 for reference, the first and last variables.

**Minimal Complete Verifiable Example**:

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

a = np.arange(0, 2000)
b = np.core.defchararray.add(""long_variable_name"", a.astype(str))
data_vars = dict()
for v in b:
    data_vars[v] = xr.DataArray(
        name=v,
        data=[3, 4],
        dims=[""time""],
        coords=dict(time=[0, 1])
    )
ds = xr.Dataset(data_vars)

# Everything above feels fast. Printing to console however takes about 13 seconds for me:
print(ds)
```

**Anything else we need to know?**:
Out of scope brainstorming:
Though printing 2000 variables is probably madness for most people it is kind of nice to show all variables because you sometimes want to know what happened to a few other variables as well. Is there already an easy and fast way to create subgroup of the dataset, so we don' have to rely on the dataset printing everything to the console everytime?

**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
OS-release: 10

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/4736/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
777526340,MDExOlB1bGxSZXF1ZXN0NTQ3Nzk5MDk2,4750,Limit number of data rows shown in repr,14371165,closed,0,,,8,2021-01-02T21:14:50Z,2021-01-04T02:13:52Z,2021-01-04T02:13:52Z,MEMBER,,0,pydata/xarray/pulls/4750,"<!-- Feel free to remove check-list items aren't relevant to your change -->

 - [x] Closes #4736
 - [x] Tests added
 - [x] Passes `isort . && black . && mypy . && flake8`
 - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst`
 - [ ] New functions/methods are listed in `api.rst`

Test example:
```python
a = np.arange(0, 2000)
b = np.core.defchararray.add(""long_variable_name"", a.astype(str))
c = np.arange(0, 30)
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=da.array([3, 4]),
        dims=[""time""],
        coords=coords,
    )
ds0 = xr.Dataset(data_vars)
ds0.attrs = e
```

Looks like this with 24 max rows of interesting data:
```python
print(ds0)
Out[15]: 
<xarray.Dataset>
Dimensions:                 (time: 2)
Coordinates:
  * time                    (time) int32 0 1
Data variables:
    long_variable_name0     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name2     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name3     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name4     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name5     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name6     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name7     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name8     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name9     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name10    (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name11    (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    ...                      ...
    long_variable_name1988  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1989  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1990  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1991  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1992  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1993  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1994  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1995  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1996  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1997  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1998  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1999  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
Attributes:
    attr_0:   2
    attr_1:   2
    attr_2:   2
    attr_3:   2
    attr_4:   2
    attr_5:   2
    attr_6:   2
    attr_7:   2
    attr_8:   2
    attr_9:   2
    attr_10:  2
    attr_11:  2
    ...       ...
    attr_18:  2
    attr_19:  2
    attr_20:  2
    attr_21:  2
    attr_22:  2
    attr_23:  2
    attr_24:  2
    attr_25:  2
    attr_26:  2
    attr_27:  2
    attr_28:  2
    attr_29:  2
```
With 20 rows of interesting data:
```python
xr.set_options(display_max_rows=20)
ds0
Out[26]: 
<xarray.Dataset>
Dimensions:                 (time: 2)
Coordinates:
  * time                    (time) int32 0 1
Data variables:
    long_variable_name0     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name2     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name3     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name4     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name5     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name6     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name7     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name8     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name9     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
                     ...
    long_variable_name1990  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1991  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1992  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1993  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1994  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1995  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1996  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1997  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1998  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1999  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
Attributes:
    attr_0:   2
    attr_1:   2
    attr_2:   2
    attr_3:   2
    attr_4:   2
    attr_5:   2
    attr_6:   2
    attr_7:   2
    attr_8:   2
    attr_9:   2
      ...
    attr_20:  2
    attr_21:  2
    attr_22:  2
    attr_23:  2
    attr_24:  2
    attr_25:  2
    attr_26:  2
    attr_27:  2
    attr_28:  2
    attr_29:  2
```

With 16 rows of interesting data:
```python 
xr.set_options(display_max_rows=16)
ds0
Out[28]: 
<xarray.Dataset>
Dimensions:                 (time: 2)
Coordinates:
  * time                    (time) int32 0 1
Data variables:
    long_variable_name0     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name2     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name3     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name4     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name5     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name6     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name7     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
                     ...
    long_variable_name1992  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1993  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1994  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1995  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1996  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1997  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1998  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1999  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
Attributes:
    attr_0:   2
    attr_1:   2
    attr_2:   2
    attr_3:   2
    attr_4:   2
    attr_5:   2
    attr_6:   2
    attr_7:   2
      ...
    attr_22:  2
    attr_23:  2
    attr_24:  2
    attr_25:  2
    attr_26:  2
    attr_27:  2
    attr_28:  2
    attr_29:  2
```


With 12 rows of interesting data:
```python
xr.set_options(display_max_rows=12)
print(ds0)
Out[79]: 
<xarray.Dataset>
Dimensions:                 (time: 2)
Coordinates:
  * time                    (time) int32 0 1
Data variables:
    long_variable_name0     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name2     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name3     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name4     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name5     (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
                     ...
    long_variable_name1994  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1995  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1996  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1997  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1998  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
    long_variable_name1999  (time) int32 dask.array<chunksize=(2,), meta=np.ndarray>
Attributes:
    attr_0:   2
    attr_1:   2
    attr_2:   2
    attr_3:   2
    attr_4:   2
    attr_5:   2
      ...
    attr_24:  2
    attr_25:  2
    attr_26:  2
    attr_27:  2
    attr_28:  2
    attr_29:  2

```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4750/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull