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
1330149534,I_kwDOAMm_X85PSHie,6881,Alignment of dataset with MultiIndex fails after applying xr.concat  ,19226431,closed,0,,,0,2022-08-05T16:42:05Z,2022-08-25T11:15:55Z,2022-08-25T11:15:55Z,CONTRIBUTOR,,,,"### What happened?

After applying the `concat` function to a dataset with a Multiindex, a lot of functions related to indexing are broken. For example, it is not possible to apply `reindex_like` to itself anymore. 

The error is raised in the alignment module. It seems that the function `find_matching_indexes` does not find indexes that belong to the same dimension. 

### What did you expect to happen?

I expected the alignment to be functional and that these basic functions work.  

### Minimal Complete Verifiable Example

```Python
import xarray as xr
import pandas as pd

index = pd.MultiIndex.from_product([[1,2], ['a', 'b']], names=('level1', 'level2'))
index.name = 'dim'

var = xr.DataArray(1, coords=[index])
ds = xr.Dataset({""var"":var})

new = xr.concat([ds], dim='newdim')
xr.Dataset(new) # breaks
new.reindex_like(new) # breaks
```


### MVCE confirmation

- [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- [X] Complete example — the example is self-contained, including all data and the text of any traceback.
- [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result.
- [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

### Relevant log output

```Python
Traceback (most recent call last):

  File ""/tmp/ipykernel_407170/4030736219.py"", line 11, in <cell line: 11>
    xr.Dataset(new) # breaks

  File ""/home/fabian/.miniconda3/lib/python3.10/site-packages/xarray/core/dataset.py"", line 599, in __init__
    variables, coord_names, dims, indexes, _ = merge_data_and_coords(

  File ""/home/fabian/.miniconda3/lib/python3.10/site-packages/xarray/core/merge.py"", line 575, in merge_data_and_coords
    return merge_core(

  File ""/home/fabian/.miniconda3/lib/python3.10/site-packages/xarray/core/merge.py"", line 752, in merge_core
    aligned = deep_align(

  File ""/home/fabian/.miniconda3/lib/python3.10/site-packages/xarray/core/alignment.py"", line 827, in deep_align
    aligned = align(

  File ""/home/fabian/.miniconda3/lib/python3.10/site-packages/xarray/core/alignment.py"", line 764, in align
    aligner.align()

  File ""/home/fabian/.miniconda3/lib/python3.10/site-packages/xarray/core/alignment.py"", line 550, in align
    self.assert_no_index_conflict()

  File ""/home/fabian/.miniconda3/lib/python3.10/site-packages/xarray/core/alignment.py"", line 319, in assert_no_index_conflict
    raise ValueError(

ValueError: cannot re-index or align objects with conflicting indexes found for the following dimensions: 'dim' (2 conflicting indexes)
Conflicting indexes may occur when
- they relate to different sets of coordinate and/or dimension names
- they don't have the same type
- they may be used to reindex data along common dimensions
```


### Anything else we need to know?

_No response_

### Environment

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 07:04:59) [GCC 10.3.0]
python-bits: 64
OS: Linux
OS-release: 5.15.0-41-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.1
libnetcdf: 4.8.1

xarray: 2022.6.0
pandas: 1.4.2
numpy: 1.21.6
scipy: 1.8.1
netCDF4: 1.6.0
pydap: None
h5netcdf: None
h5py: 3.6.0
Nio: None
zarr: None
cftime: 1.5.1.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.10
cfgrib: None
iris: None
bottleneck: 1.3.4
dask: 2022.6.1
distributed: 2022.6.1
matplotlib: 3.5.1
cartopy: 0.20.2
seaborn: 0.11.2
numbagg: None
fsspec: 2022.3.0
cupy: None
pint: None
sparse: 0.13.0
flox: None
numpy_groupies: None
setuptools: 61.2.0
pip: 22.1.2
conda: 4.13.0
pytest: 7.1.2
IPython: 7.33.0
sphinx: 5.0.2
/home/fabian/.miniconda3/lib/python3.10/site-packages/_distutils_hack/__init__.py:30: UserWarning: Setuptools is replacing distutils.
  warnings.warn(""Setuptools is replacing distutils."")

</details>
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784042442,MDU6SXNzdWU3ODQwNDI0NDI=,4796,Use apply_ufunc for unary funcs,19226431,open,0,,,3,2021-01-12T08:56:03Z,2022-04-18T16:31:02Z,,CONTRIBUTOR,,,,"<!-- 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.
-->

DataArray.clip() of a chunked array returns an assertion error as soon as the argument takes an chunked array. With non-chunked arrays every thing works as intended. 

```python
x = xr.DataArray(np.random.uniform(size=[100, 100])).chunk(10)
x.clip(max=x)
```

**Environment**:

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

<!-- Paste the output here xr.show_versions() here -->

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.9 | packaged by conda-forge | (default, Dec  9 2020, 21:08:20) 
[GCC 9.3.0]
python-bits: 64
OS: Linux
OS-release: 5.4.0-60-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.3

xarray: 0.16.2
pandas: 1.2.0
numpy: 1.19.5
scipy: 1.5.0
netCDF4: 1.5.3
pydap: None
h5netcdf: 0.7.4
h5py: 2.10.0
Nio: None
zarr: 2.3.2
cftime: 1.0.4.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.1.0
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 2020.12.0
distributed: 2020.12.0
matplotlib: 3.1.3
cartopy: 0.18.0
seaborn: 0.11.0
numbagg: None
pint: None
setuptools: 49.2.1.post20200807
pip: 20.2.1
conda: 4.8.3
pytest: 6.0.1
IPython: 7.11.1
sphinx: 3.1.2

</details>
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830040696,MDU6SXNzdWU4MzAwNDA2OTY=,5024,xr.DataArray.sum() converts string objects into unicode,19226431,open,0,,,0,2021-03-12T11:47:06Z,2022-04-09T01:40:09Z,,CONTRIBUTOR,,,,"<!-- 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**:

When summing over all axes of a DataArray with strings of dtype `object`, the result is a one-size `unicode` DataArray. 

**What you expected to happen**:

I expected the summation would preserve the dtype, meaning the one-size DataArray would be of dtype `object`

**Minimal Complete Verifiable Example**:


```
ds = xr.DataArray('a', [range(3), range(3)]).astype(object)
ds.sum()
```

Output
```
  <xarray.DataArray ()>
    array('aaaaaaaaa', dtype='<U9')
```

On the other hand, when summing over one dimension only, the dtype is preserved
```
ds.sum('dim_0')
```

Output:
```
   <xarray.DataArray (dim_1: 3)>
    array(['aaa', 'aaa', 'aaa'], dtype=object)
    Coordinates:
      * dim_1    (dim_1) int64 0 1 2
```
 
**Anything else we need to know?**:

The problem becomes relevant as soon as dask is used in the workflow. Dask expects the aggregated DataArray to be of dtype `object` which will likely lead to errors in the operations to follow. 

Probably the behavior comes from creating a new DataArray after the reduction with `np.sum()` (which itself leads results in a pure python string).   

**Environment**:

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

<!-- Paste the output here xr.show_versions() here -->
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.5 (default, Sep  4 2020, 07:30:14) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 5.4.0-66-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.6
libnetcdf: 4.7.4

xarray: 0.16.2
pandas: 1.2.1
numpy: 1.19.5
scipy: 1.6.0
netCDF4: 1.5.5.1
pydap: None
h5netcdf: 0.7.4
h5py: 3.1.0
Nio: None
zarr: 2.3.2
cftime: 1.3.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.0
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2021.01.1
distributed: 2021.01.1
matplotlib: 3.3.3
cartopy: 0.18.0
seaborn: 0.11.1
numbagg: None
pint: None
setuptools: 52.0.0.post20210125
pip: 21.0
conda: 4.9.2
pytest: 6.2.2
IPython: 7.19.0
sphinx: 3.4.3

</details>
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1052736383,I_kwDOAMm_X84-v3t_,5983,preserve chunked data when creating DataArray from itself ,19226431,closed,0,,,4,2021-11-13T18:00:24Z,2022-01-13T17:02:47Z,2022-01-13T17:02:47Z,CONTRIBUTOR,,,,"**What happened**:

When creating a new DataArray from a DataArray with chunked data, the underlying dask array is converted to a numpy array.  

**What you expected to happen**:

I expected the underlying dask array to be preseved when creating a new DataArray instance.

**Minimal Complete Verifiable Example**:

```python
import xarray as xr
import numpy as np
from dask import array

d = np.ones((10, 10))
x = array.from_array(d, chunks=5)

da = xr.DataArray(x) # this is chunked
xr.DataArray(da) # this is not chunked anymore
```

**Anything else we need to know?**:

**Environment**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>
INSTALLED VERSIONS
------------------
commit: None
python: 3.9.7 (default, Sep 16 2021, 13:09:58) 
[GCC 7.5.0]
python-bits: 64
OS: Linux
OS-release: 5.11.0-40-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.10.6
libnetcdf: 4.7.4

xarray: 0.19.0
pandas: 1.3.3
numpy: 1.20.3
scipy: 1.7.1
netCDF4: 1.5.6
pydap: None
h5netcdf: 0.11.0
h5py: 3.2.1
Nio: None
zarr: 2.10.1
cftime: 1.5.0
nc_time_axis: None
PseudoNetCDF: None
rasterio: 1.2.6
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2021.09.1
distributed: 2021.09.1
matplotlib: 3.4.3
cartopy: 0.19.0.post1
seaborn: 0.11.2
numbagg: None
pint: None
setuptools: 58.0.4
pip: 21.2.4
conda: 4.10.3
pytest: 6.2.5
IPython: 7.27.0
sphinx: 4.2.0
<!-- Paste the output here xr.show_versions() here -->


</details>
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