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
1953053810,PR_kwDOAMm_X85dURGi,8344,Add mean to NamedArray._array_api,14371165,open,0,,,8,2023-10-19T21:05:06Z,2023-12-19T17:49:22Z,,MEMBER,,1,pydata/xarray/pulls/8344,"<!-- Feel free to remove check-list items aren't relevant to your change -->

- [ ] Closes #xxxx
- [ ] Tests added
- [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst`
- [ ] New functions/methods are listed in `api.rst`
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8344/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
970245117,MDExOlB1bGxSZXF1ZXN0NzEyMjIzNzc2,5704,Allow in-memory arrays with open_mfdataset,14371165,open,0,,,8,2021-08-13T09:50:26Z,2023-04-29T06:58:26Z,,MEMBER,,0,pydata/xarray/pulls/5704,"The docstring seems to imply that it's possible to get in-memory arrays:
https://github.com/pydata/xarray/blob/4bb9d9c6df77137f05e85c7cc6508fe7a93dc0e4/xarray/backends/api.py#L732

But it doesn't seem possible because of:
https://github.com/pydata/xarray/blob/4bb9d9c6df77137f05e85c7cc6508fe7a93dc0e4/xarray/backends/api.py#L899

This PR removes that `or` check, changes the default to `chunk={}`, and fixes the failing tests.

<!-- Feel free to remove check-list items aren't relevant to your change -->

- [x] Noticed in  #5689
- [ ] Closes #7792
- [x] Tests added
- [x] Passes `pre-commit run --all-files`
- [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst`
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5704/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
931016490,MDExOlB1bGxSZXF1ZXN0Njc4NTc5MjIx,5542,Do not transpose 1d arrays during interpolation,14371165,open,0,,,8,2021-06-27T20:56:13Z,2022-10-12T20:12:11Z,,MEMBER,,0,pydata/xarray/pulls/5542,"Seems a waste of time to transpose 1d arrays.

<!-- Feel free to remove check-list items aren't relevant to your change -->

- [ ] Closes #xxxx
- [ ] Tests added
- [ ] Passes `pre-commit run --all-files`
- [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst`
- [ ] New functions/methods are listed in `api.rst`
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5542/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
957432870,MDExOlB1bGxSZXF1ZXN0NzAwODYwMzY4,5661,Speed up _mapping_repr,14371165,closed,0,,,8,2021-08-01T08:44:17Z,2022-08-12T09:07:44Z,2021-08-02T19:45:16Z,MEMBER,,0,pydata/xarray/pulls/5661,"Creating a ordered list for filtering purposes using `.items()` turns out being rather slow. Use `.keys()` instead as that doesn't trigger a bunch of dataarray initializations.

- [x] Passes `pre-commit run --all-files`

Test case:

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

a = np.arange(0, 2000)
data_vars = dict()
for i in a:
    data_vars[f""long_variable_name_{i}""] = xr.DataArray(
        name=f""long_variable_name_{i}"",
        data=np.arange(0, 20),
        dims=[f""long_coord_name_{i}_x""],
        coords={f""long_coord_name_{i}_x"": np.arange(0, 20) * 2},
    )
ds0 = xr.Dataset(data_vars)
ds0.attrs = {f""attr_{k}"": 2 for k in a}
```


Before:
```python
%timeit print(ds0)
14.6 s ± 215 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

After:
```python
%timeit print(ds0)
120 ms ± 2.06 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5661/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
830918156,MDExOlB1bGxSZXF1ZXN0NTkyMzc2Mzk2,5031,Keep coord attrs when interpolating,14371165,closed,0,,,8,2021-03-13T15:05:39Z,2021-05-18T18:16:10Z,2021-04-27T07:00:08Z,MEMBER,,0,pydata/xarray/pulls/5031,"<!-- Feel free to remove check-list items aren't relevant to your change -->

- [x] Closes #4239 
- [x] Closes #4839
- [x] Tests added
- [x] Passes `pre-commit run --all-files`
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5031/reactions"", ""total_count"": 2, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 1, ""eyes"": 0}",,,13221727,pull
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