What happened?

I have just updated my development branch of xarray to latest main. No other changes. When using .chunk() on a Variable xarray crashes.

What did you expect to happen?

No crash

Minimal Complete Verifiable Example

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

t_size = 8000 t = np.arange(t_size) var = xr.Variable(dims=("T",), data=np.random.randn(t_size)).chunk() ```

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, 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 "C:\Users\J.W\AppData\Local\Temp\ipykernel_6480\4053253683.py", line 8, in <cell line: 8> var = xr.Variable(dims=("T",), data=np.random.randn(t_size)).chunk()

File "C:\Users\J.W\Documents\GitHub\xarray\xarray\core\variable.py", line 1249, in chunk chunkmanager = guess_chunkmanager(chunked_array_type)

File "C:\Users\J.W\Documents\GitHub\xarray\xarray\core\parallelcompat.py", line 87, in guess_chunkmanager raise ValueError(

ValueError: unrecognized chunk manager dask - must be one of: [] ```

Anything else we need to know?

Likely from #7019.

Environment

What happened?

The CI seems to consistently crash on test_units.py now: =========================== short test summary info =========================== FAILED xarray/tests/test_units.py::TestVariable::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestVariable::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[float64-function_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[float64-function_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-function_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-function_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataArray::test_unary_operations[float64-round] - TypeError: no implementation found for 'numpy.round' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataArray::test_unary_operations[int32-round] - TypeError: no implementation found for 'numpy.round' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataset::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [<class 'pint.util.Quantity'>] FAILED xarray/tests/test_units.py::TestDataset::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [<class 'pint.util.Quantity'>] = 12 failed, 14880 passed, 1649 skipped, 146 xfailed, 68 xpassed, 574 warnings in 737.19s (0:12:17) = For more details: https://github.com/pydata/xarray/actions/runs/5438369625/jobs/9889561685?pr=7955

Matplotlib has started to support typing in main (https://github.com/matplotlib/matplotlib/issues/20504) and mypy is throwing a few errors:

Some guidance how to solve these:

• [xy]ticks in mpl is currently overly narrowly type hinted because I was following the docstring, but I agree that ArrayLike is a better type hint for that, plan on updating (including the docstring) upstream
• [xy]lim originally neglected the case of passing set_xlim((min, max)) as a tuple, but that has been updated. xarray has that type hinted as array like, but mpl has it hinted as a 2-tuple (I think it is currently still of floats, but may be expanded as we more directly address units/categoricals/etc). Willing to debate here, but my starting position is that the "exactly 2 values" is valuable info here, and I think tuple is the only way to do that.
• get_renderer is not actually available on all of our backends, we should maybe see if there is a more preferred way of doing what you are doing here that will work for all backends, but haven't looked into it too closely.
• Module has no attribute <colormap> is another instance of dynamically generated behavior which can't be statically type checked (elegantly, at least), can probably be replaced by mpl.colormaps["<colormap>"] in many cases, which is statically typecheckable
• Anything to do with 3D Axes is not type hinted, perhaps ignore for now (or help us get that type hinted adequately, but it is relatively low priority currently)
• Module has no attribute "dates" we don't currently type hint dates/units things, but it is on my mind, not sure yet if it will be in first release or not though (may at least put a placeholder that gets rid of this error, but treats everything as "Any").

Originally posted by @ksunden in https://github.com/pydata/xarray/issues/7787#issuecomment-1523743471

The suggestion from mpl (specifically @tacaswell) was to use constrained layout for the purpose that xarray currently uses get_renderer, this will ensure that the facetgrid works with all mpl backends.

Originally posted by @ksunden in https://github.com/pydata/xarray/issues/7787#issuecomment-1528091492

I'm also relatively sure that if you are willing to put a floor on the version of Matplotlib you support get_window_extent will use it's internally cached renderer (and when we make it uniformly optional we also fixed the cache invalidation logic).

Originally posted by @tacaswell in https://github.com/pydata/xarray/issues/7787#issuecomment-1528096647

What happened?

The upstream CI has been failing for a while. Here's the latest: https://github.com/pydata/xarray/actions/runs/5501368493/jobs/10024902009#step:7:16

python Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/runner/work/xarray/xarray/xarray/__init__.py", line 1, in <module> from xarray import testing, tutorial File "/home/runner/work/xarray/xarray/xarray/testing.py", line 7, in <module> import numpy as np ModuleNotFoundError: No module named 'numpy'

Digging a little in the logs ``` Installing build dependencies: started Installing build dependencies: finished with status 'error' error: subprocess-exited-with-error

× pip subprocess to install build dependencies did not run successfully. │ exit code: 1 ╰─> [3 lines of output] Looking in indexes: https://pypi.anaconda.org/scipy-wheels-nightly/simple ERROR: Could not find a version that satisfies the requirement meson-python==0.13.1 (from versions: none) ERROR: No matching distribution found for meson-python==0.13.1 [end of output] ```

Might be some numpy problem?

Should the CI be robust enough to handle these kinds of errors? Because I suppose we would like to get the automatic issue created anyway?

Is your feature request related to a problem?

Use more up-to-date typing styles on all files. Will reduce number of imports and avoids big diffs when doing relatively minor changes because pre-commit/pyupgrade has been triggered somehow.

Related to #6240

Describe the solution you'd like

Add from __future__ import annotations on files with a lot of typing. Let pyupgrade do the rest.

Describe alternatives you've considered

No response

Additional context

No response

Is your feature request related to a problem?

I was making sure the test in #6832 failed on main, when it did I wrote a few lines in the what's new file but forgot switching back to the other branch and accidentally pushed directly to main. :(

Describe the solution you'd like

I think it's best if we require a pull request for merging. We seem to pretty much do this anyway.

Seems to be this setting if I understand correctly:

Describe alternatives you've considered

No response

Additional context

No response

What is your issue?

As I've understood it ZarrBackendEntrypoint is the engine used when loading zarr-files. But for some reason we show ZarrStore in xr.backends. I believe the ZarrStore class is supposed to be just a implementation detail, right?

```python

The available engines:

xr.backends.list_engines() Out[23]: {'netcdf4': <xarray.backends.netCDF4_.NetCDF4BackendEntrypoint object at 0x00000296D05D11F0>, 'h5netcdf': <xarray.backends.h5netcdf_.H5netcdfBackendEntrypoint object at 0x00000296D05D14C0>, 'scipy': <xarray.backends.scipy_.ScipyBackendEntrypoint object at 0x00000296D05D11C0>, 'pseudonetcdf': <xarray.backends.pseudonetcdf_.PseudoNetCDFBackendEntrypoint object at 0x00000296D05D1430>, 'pydap': <xarray.backends.pydap_.PydapBackendEntrypoint object at 0x00000296D05D1310>, 'store': <xarray.backends.store.StoreBackendEntrypoint object at 0x00000296D05D1340>, 'zarr': <xarray.backends.zarr.ZarrBackendEntrypoint object at 0x00000296D05D12E0>}

The public class is ZarrStore instead of ZarrBackendEntrypoint, how come?

dir(xr.backends) Out[22]: ['AbstractDataStore', 'BackendArray', 'BackendEntrypoint', 'CachingFileManager', 'CfGribDataStore', 'DummyFileManager', 'FileManager', 'H5NetCDFStore', 'InMemoryDataStore', 'NetCDF4DataStore', 'NioDataStore', 'PseudoNetCDFDataStore', 'PydapDataStore', 'ScipyDataStore', 'ZarrStore', 'all', 'builtins', 'cached', 'doc', 'file', 'loader', 'name', 'package', 'path', 'spec', 'api', 'cfgrib_', 'common', 'file_manager', 'h5netcdf_', 'list_engines', 'locks', 'lru_cache', 'memory', 'netCDF4_', 'netcdf3', 'plugins', 'pseudonetcdf_', 'pydap_', 'pynio_', 'rasterio_', 'scipy_', 'store', 'zarr'] ```

Is your feature request related to a problem? Please describe. To make it easier reading the typing it is now possible to use | instead of Union or Optional.

Here's an example how it looks like in pandas: https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_core.py#L116-L134

Describe the solution you'd like Replace for example: * Union[str, int] with str | int * Optional[str] with None | str

This would likely require adding from __future__ import annotations at the top of the file.

References https://www.python.org/dev/peps/pep-0604/

What happened?

xr.concat removes datetime information and can't concatenate the arrays because they don't have compatible types anymore.

What did you expect to happen?

Succesful concatenation with the same type.

Minimal Complete Verifiable Example

```Python import numpy as np import xarray as xr from datetime import datetime

month = np.arange(1, 13, 1) data = np.sin(2 * np.pi * month / 12.0)

darray = xr.DataArray(data, dims=["time"]) darray.coords["time"] = np.array([datetime(2017, m, 1) for m in month])

darray_nan = np.nan * darray.isel(**{"time": -1}) darray = xr.concat([darray, darray_nan], dim="time") ```

Relevant log output

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

File "<ipython-input-15-31040255a336>", line 2, in <module> darray = xr.concat([darray, darray_nan], dim="time")

File "c:\users\j.w\documents\github\xarray\xarray\core\concat.py", line 244, in concat return f(

File "c:\users\j.w\documents\github\xarray\xarray\core\concat.py", line 642, in _dataarray_concat ds = _dataset_concat(

File "c:\users\j.w\documents\github\xarray\xarray\core\concat.py", line 555, in _dataset_concat combined_idx = indexes[0].concat(indexes, dim, positions)

File "c:\users\j.w\documents\github\xarray\xarray\core\indexes.py", line 318, in concat coord_dtype = np.result_type(*[idx.coord_dtype for idx in indexes])

File "<array_function internals>", line 5, in result_type

TypeError: The DType <class 'numpy.dtype[datetime64]'> could not be promoted by <class 'numpy.dtype[int64]'>. This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is object. The full list of DTypes is: (<class 'numpy.dtype[datetime64]'>, <class 'numpy.dtype[int64]'>) ```

Anything else we need to know?

Similar to #6384.

Happens around here:

https://github.com/pydata/xarray/blob/728b648d5c7c3e22fe3704ba163012840408bf66/xarray/core/concat.py#L535

Environment

What happened?

When concatenating dataarrays with pd.Interval along a dim the pd.Interval is wrapped with a numpy array and appended instead of without like it it was before #5692.

Minimal Complete Verifiable Example

```Python import numpy as np import xarray as xr

shape = (2, 3, 4) darray = xr.DataArray(np.linspace(0, 1, num=np.prod(shape)).reshape(shape)) bins = [-1, 0, 1, 2] a = darray.groupby_bins("dim_0", bins).mean(...) a_nan = np.nan * a.isel(**{"dim_0_bins": -1}) out = xr.concat([a, a_nan], dim="dim_0_bins") print(out["dim_0_bins"]) ```

Relevant log output

Current result: Python <xarray.DataArray 'dim_0_bins' (dim_0_bins: 4)> array([Interval(-1, 0, closed='right'), Interval(0, 1, closed='right'), Interval(1, 2, closed='right'), array(Interval(1, 2, closed='right'), dtype=object)], dtype=object) Coordinates: * dim_0_bins (dim_0_bins) object (-1, 0] (0, 1] (1, 2] (1, 2]

Should be: ```python <xarray.DataArray 'dim_0_bins' (dim_0_bins: 4)> array([Interval(-1, 0, closed='right'), Interval(0, 1, closed='right'), Interval(1, 2, closed='right'), Interval(1, 2, closed='right')], dtype=object) Coordinates: * dim_0_bins (dim_0_bins) object (-1, 0] (0, 1] (1, 2] (1, 2]

```

Anything else we need to know?

No response

Environment

What happened: Dimension doesn't seem to be limited when there are too many of them. See example below. This slows down the repr significantly and is quite unreadable to me.

What you expected to happen: To be limited so that it aligns with whatever the maximum line length is for variables. It's also fine if it continues on a couple of rows below in similar fashion to variables.

Minimal Complete Verifiable Example: This is probably a bit of an edge case. My real datasets usually have around 12 "dimensions" and coords, +2000 variables, 50 attrs. python 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.array([3, 4]), dims=[f"long_coord_name_{i}_x"], coords={f"long_coord_name_{i}_x": np.array([0, 1])}, ) ds0 = xr.Dataset(data_vars) ds0.attrs = {f"attr_{k}": 2 for k in a}

python <xarray.Dataset> Dimensions: (long_coord_name_0_x: 2, long_coord_name_1000_x: 2, long_coord_name_1001_x: 2, long_coord_name_1002_x: 2, long_coord_name_1003_x: 2, long_coord_name_1004_x: 2, long_coord_name_1005_x: 2, long_coord_name_1006_x: 2, long_coord_name_1007_x: 2, long_coord_name_1008_x: 2, long_coord_name_1009_x: 2, long_coord_name_100_x: 2, long_coord_name_1010_x: 2, long_coord_name_1011_x: 2, long_coord_name_1012_x: 2, long_coord_name_1013_x: 2, long_coord_name_1014_x: 2, long_coord_name_1015_x: 2, long_coord_name_1016_x: 2, long_coord_name_1017_x: 2, long_coord_name_1018_x: 2, long_coord_name_1019_x: 2, long_coord_name_101_x: 2, long_coord_name_1020_x: 2, long_coord_name_1021_x: 2, long_coord_name_1022_x: 2, long_coord_name_1023_x: 2, long_coord_name_1024_x: 2, long_coord_name_1025_x: 2, long_coord_name_1026_x: 2, long_coord_name_1027_x: 2, long_coord_name_1028_x: 2, long_coord_name_1029_x: 2, long_coord_name_102_x: 2, long_coord_name_1030_x: 2, long_coord_name_1031_x: 2, long_coord_name_1032_x: 2, long_coord_name_1033_x: 2, long_coord_name_1034_x: 2, long_coord_name_1035_x: 2, long_coord_name_1036_x: 2, long_coord_name_1037_x: 2, long_coord_name_1038_x: 2, long_coord_name_1039_x: 2, long_coord_name_103_x: 2, long_coord_name_1040_x: 2, long_coord_name_1041_x: 2, long_coord_name_1042_x: 2, long_coord_name_1043_x: 2, long_coord_name_1044_x: 2, long_coord_name_1045_x: 2, long_coord_name_1046_x: 2, long_coord_name_1047_x: 2, long_coord_name_1048_x: 2, long_coord_name_1049_x: 2, long_coord_name_104_x: 2, long_coord_name_1050_x: 2, long_coord_name_1051_x: 2, long_coord_name_1052_x: 2, long_coord_name_1053_x: 2, long_coord_name_1054_x: 2, long_coord_name_1055_x: 2, long_coord_name_1056_x: 2, long_coord_name_1057_x: 2, long_coord_name_1058_x: 2, long_coord_name_1059_x: 2, long_coord_name_105_x: 2, long_coord_name_1060_x: 2, long_coord_name_1061_x: 2, long_coord_name_1062_x: 2, long_coord_name_1063_x: 2, long_coord_name_1064_x: 2, long_coord_name_1065_x: 2, long_coord_name_1066_x: 2, long_coord_name_1067_x: 2, long_coord_name_1068_x: 2, long_coord_name_1069_x: 2, long_coord_name_106_x: 2, long_coord_name_1070_x: 2, long_coord_name_1071_x: 2, long_coord_name_1072_x: 2, long_coord_name_1073_x: 2, long_coord_name_1074_x: 2, long_coord_name_1075_x: 2, long_coord_name_1076_x: 2, long_coord_name_1077_x: 2, long_coord_name_1078_x: 2, long_coord_name_1079_x: 2, long_coord_name_107_x: 2, long_coord_name_1080_x: 2, long_coord_name_1081_x: 2, long_coord_name_1082_x: 2, long_coord_name_1083_x: 2, long_coord_name_1084_x: 2, long_coord_name_1085_x: 2, long_coord_name_1086_x: 2, long_coord_name_1087_x: 2, long_coord_name_1088_x: 2, long_coord_name_1089_x: 2, long_coord_name_108_x: 2, long_coord_name_1090_x: 2, long_coord_name_1091_x: 2, long_coord_name_1092_x: 2, long_coord_name_1093_x: 2, long_coord_name_1094_x: 2, long_coord_name_1095_x: 2, long_coord_name_1096_x: 2, long_coord_name_1097_x: 2, long_coord_name_1098_x: 2, long_coord_name_1099_x: 2, long_coord_name_109_x: 2, long_coord_name_10_x: 2, long_coord_name_1100_x: 2, long_coord_name_1101_x: 2, long_coord_name_1102_x: 2, long_coord_name_1103_x: 2, long_coord_name_1104_x: 2, long_coord_name_1105_x: 2, long_coord_name_1106_x: 2, long_coord_name_1107_x: 2, long_coord_name_1108_x: 2, long_coord_name_1109_x: 2, long_coord_name_110_x: 2, long_coord_name_1110_x: 2, long_coord_name_1111_x: 2, long_coord_name_1112_x: 2, long_coord_name_1113_x: 2, long_coord_name_1114_x: 2, long_coord_name_1115_x: 2, long_coord_name_1116_x: 2, long_coord_name_1117_x: 2, long_coord_name_1118_x: 2, long_coord_name_1119_x: 2, long_coord_name_111_x: 2, long_coord_name_1120_x: 2, long_coord_name_1121_x: 2, long_coord_name_1122_x: 2, long_coord_name_1123_x: 2, long_coord_name_1124_x: 2, long_coord_name_1125_x: 2, long_coord_name_1126_x: 2, long_coord_name_1127_x: 2, long_coord_name_1128_x: 2, long_coord_name_1129_x: 2, long_coord_name_112_x: 2, long_coord_name_1130_x: 2, long_coord_name_1131_x: 2, long_coord_name_1132_x: 2, long_coord_name_1133_x: 2, long_coord_name_1134_x: 2, long_coord_name_1135_x: 2, long_coord_name_1136_x: 2, long_coord_name_1137_x: 2, long_coord_name_1138_x: 2, long_coord_name_1139_x: 2, long_coord_name_113_x: 2, long_coord_name_1140_x: 2, long_coord_name_1141_x: 2, long_coord_name_1142_x: 2, long_coord_name_1143_x: 2, long_coord_name_1144_x: 2, long_coord_name_1145_x: 2, long_coord_name_1146_x: 2, long_coord_name_1147_x: 2, long_coord_name_1148_x: 2, long_coord_name_1149_x: 2, long_coord_name_114_x: 2, long_coord_name_1150_x: 2, long_coord_name_1151_x: 2, long_coord_name_1152_x: 2, long_coord_name_1153_x: 2, long_coord_name_1154_x: 2, long_coord_name_1155_x: 2, long_coord_name_1156_x: 2, long_coord_name_1157_x: 2, long_coord_name_1158_x: 2, long_coord_name_1159_x: 2, long_coord_name_115_x: 2, long_coord_name_1160_x: 2, long_coord_name_1161_x: 2, long_coord_name_1162_x: 2, long_coord_name_1163_x: 2, long_coord_name_1164_x: 2, long_coord_name_1165_x: 2, 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long_coord_name_951_x: 2, long_coord_name_952_x: 2, long_coord_name_953_x: 2, long_coord_name_954_x: 2, long_coord_name_955_x: 2, long_coord_name_956_x: 2, long_coord_name_957_x: 2, long_coord_name_958_x: 2, long_coord_name_959_x: 2, long_coord_name_95_x: 2, long_coord_name_960_x: 2, long_coord_name_961_x: 2, long_coord_name_962_x: 2, long_coord_name_963_x: 2, long_coord_name_964_x: 2, long_coord_name_965_x: 2, long_coord_name_966_x: 2, long_coord_name_967_x: 2, long_coord_name_968_x: 2, long_coord_name_969_x: 2, long_coord_name_96_x: 2, long_coord_name_970_x: 2, long_coord_name_971_x: 2, long_coord_name_972_x: 2, long_coord_name_973_x: 2, long_coord_name_974_x: 2, long_coord_name_975_x: 2, long_coord_name_976_x: 2, long_coord_name_977_x: 2, long_coord_name_978_x: 2, long_coord_name_979_x: 2, long_coord_name_97_x: 2, long_coord_name_980_x: 2, long_coord_name_981_x: 2, long_coord_name_982_x: 2, long_coord_name_983_x: 2, long_coord_name_984_x: 2, long_coord_name_985_x: 2, long_coord_name_986_x: 2, long_coord_name_987_x: 2, long_coord_name_988_x: 2, long_coord_name_989_x: 2, long_coord_name_98_x: 2, long_coord_name_990_x: 2, long_coord_name_991_x: 2, long_coord_name_992_x: 2, long_coord_name_993_x: 2, long_coord_name_994_x: 2, long_coord_name_995_x: 2, long_coord_name_996_x: 2, long_coord_name_997_x: 2, long_coord_name_998_x: 2, long_coord_name_999_x: 2, long_coord_name_99_x: 2, long_coord_name_9_x: 2) Coordinates: (12/2000) * long_coord_name_0_x (long_coord_name_0_x) int32 0 1 * long_coord_name_1_x (long_coord_name_1_x) int32 0 1 * long_coord_name_2_x (long_coord_name_2_x) int32 0 1 * long_coord_name_3_x (long_coord_name_3_x) int32 0 1 * long_coord_name_4_x (long_coord_name_4_x) int32 0 1 * long_coord_name_5_x (long_coord_name_5_x) int32 0 1 ... * long_coord_name_1994_x (long_coord_name_1994_x) int32 0 1 * long_coord_name_1995_x (long_coord_name_1995_x) int32 0 1 * long_coord_name_1996_x (long_coord_name_1996_x) int32 0 1 * long_coord_name_1997_x (long_coord_name_1997_x) int32 0 1 * long_coord_name_1998_x (long_coord_name_1998_x) int32 0 1 * long_coord_name_1999_x (long_coord_name_1999_x) int32 0 1 Data variables: (12/2000) long_variable_name_0 (long_coord_name_0_x) int32 3 4 long_variable_name_1 (long_coord_name_1_x) int32 3 4 long_variable_name_2 (long_coord_name_2_x) int32 3 4 long_variable_name_3 (long_coord_name_3_x) int32 3 4 long_variable_name_4 (long_coord_name_4_x) int32 3 4 long_variable_name_5 (long_coord_name_5_x) int32 3 4 ... long_variable_name_1994 (long_coord_name_1994_x) int32 3 4 long_variable_name_1995 (long_coord_name_1995_x) int32 3 4 long_variable_name_1996 (long_coord_name_1996_x) int32 3 4 long_variable_name_1997 (long_coord_name_1997_x) int32 3 4 long_variable_name_1998 (long_coord_name_1998_x) int32 3 4 long_variable_name_1999 (long_coord_name_1999_x) int32 3 4 Attributes: (12/2000) attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_1994: 2 attr_1995: 2 attr_1996: 2 attr_1997: 2 attr_1998: 2 attr_1999: 2

Anything else we need to know?:

Environment:

A while back GitHub made it so that new contributors cannot trigger GitHub Actions workflows and a maintainer has to hit "Approve and Run" every time they push a commit to their PR. This is rather annoying for both the contributor and the maintainer as the back and forth takes time.

It however seems possible to relax this constraint: https://twitter.com/metcalfc/status/1448414192285806592?t=maeChQZTSUh2Ph0YFk-hGA&s=19

Shall we relax this constraint?

ref: https://github.com/dask/community/issues/191

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_likeuse 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:

What happened: When interpolating a dataset with >2000 dask variables a lot of time is spent in da.unifying_chunks because da.unifying_chunks forces all variables and coordinates to a dask array. xarray on the other hand forces coordinates to pd.Index even if the coordinates was dask.array when the dataset was first created.

What you expected to happen: If the coords of the dataset was initialized as dask arrays they should stay lazy.

Minimal Complete Verifiable Example:

```python import xarray as xr import numpy as np import dask.array as da

a = np.arange(0, 2000) 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=da.array([3, 4]), dims=["time"], coords=coords ) ds0 = xr.Dataset(data_vars) ds0 = ds0.interp( time=da.array([0, 0.5, 1]), assume_sorted=True, kwargs=dict(fill_value=None), ) ```

Anything else we need to know?: Some thoughts: * Why can't coordinates be lazy? * Can we use dask.dataframe.Index instead of pd.Index when creating IndexVariables? * There's no time saved converting to dask arrays in missing.interp_func. But some time could be saved if we could convert them to dask arrays in xr.Dataset.interp before the variable loop starts. * Can we still store the dask array in IndexVariable and use a to_dask_array()-method to quickly get it? * Initializing the dataarrays will still be slow though since it still has to force the dask array to pd.Index.

Environment:

What happened: When I was interpolating datasets I noticed that the coordinate variables disappeared.

What you expected to happen: Coordinate attributes should be retained just like variables are.

Minimal Complete Verifiable Example:

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

names = np.core.defchararray.add("long_variable_name", np.arange(0, 2).astype(str)) coords = dict(time=np.array([0, 1])) data_vars = dict() for v in names: data_vars[v] = xr.Variable( "time", np.array([0, 1], dtype=int), attrs=dict(unit="kg") ) ds1 = xr.Dataset(data_vars=data_vars, coords=coords) ds1.attrs = { k: 2 for k in np.core.defchararray.add("attr_", np.arange(0, 3).astype(str)) } ds1.time.attrs.update(unit="s")

Print time:

ds1.time Out[115]: <xarray.DataArray 'time' (time: 2)> array([0, 1]) Coordinates: * time (time) int32 0 1 Attributes: unit: s

Interpolate:

ds1 = ds1.interp( time=np.array([0, 0.5, 1, 2]), assume_sorted=True, method="linear", kwargs=dict(fill_value="extrapolate"), )

Print interpolated time, units are lost:

ds1.time Out[117]: <xarray.DataArray 'time' (time: 4)> array([0. , 0.5, 1. , 2. ]) Coordinates: * time (time) float64 0.0 0.5 1.0 2. ```

Anything else we need to know?:

Environment:

Is your feature request related to a problem? Please describe. I've been working on opening custom hdf formats in xarray, reading up on the apiv2 it is currently only possible to declare a new external plugin in setup.py but that doesn't seem easy or intuitive to me.

Describe the solution you'd like Why can't we simply be allowed to add functions to the engine parameter? Example: ```python from custom_backend import engine

ds = xr.load_dataset(filename, engine=engine) ``` This seems like a small function change to me from my initial quick look because there's mainly a bunch of string checks in the normal case until we get to the registered backend functions, if we send in a function instead in the engine-parameter we can just bypass those checks.

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:

Is your feature request related to a problem? Please describe. When creating docs with examples it would be nice if you could simply use print(ds) without being concerned about line lengths.

Describe the solution you'd like Limit line length so that a method docstring in a class can use print(ds) without breaking PEP8 conventions. So maximum line length for the ds.__repr__/ds.__str__ would be 79 - 4 - 4 = 71.

Example code Example where print(ds) creates lines longer than 79: ```python import numpy as np import pandas as pd import xarray as xr

class foo(): """ Test class.

(...)
"""

def bar():
    """
    Return 1.

    Examples
    --------
    Create data:

    >>> np.random.seed(0)
    >>> temperature = 15 + 8 * np.random.randn(2, 2, 3)
    >>> precipitation = 10 * np.random.rand(2, 2, 3)
    >>> lon = [[-99.83, -99.32], [-99.79, -99.23]]
    >>> lat = [[42.25, 42.21], [42.63, 42.59]]
    >>> time = pd.date_range("2014-09-06", periods=3)
    >>> reference_time = pd.Timestamp("2014-09-05")

    Initialize a dataset with multiple dimensions:

    >>> ds = xr.Dataset(
    ...     {
    ...         "temperature": (["x", "y", "time"], temperature),
    ...         "precipitation": (["x", "y", "time"], precipitation),
    ...     },
    ...     coords={
    ...         "lon": (["x", "y"], lon),
    ...         "lat": (["x", "y"], lat),
    ...         "time": time,
    ...         "reference_time": reference_time,
    ...     },
    ... )

    Print results:

    >>> print(ds.temperature.values[0])
    [[29.11241877 18.20125767 22.82990387]
     [32.92714559 29.94046392  7.18177696]]
    >>> print(ds)
    <xarray.Dataset>
    Dimensions:         (time: 3, x: 2, y: 2)
    Coordinates:
        lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
        lat             (x, y) float64 42.25 42.21 42.63 42.59
      * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
        reference_time  datetime64[ns] 2014-09-05
    Dimensions without coordinates: x, y
    Data variables:
        temperature     (x, y, time) float64 29.11 18.2 22.83 ... 18.28 16.15 26.63
        precipitation   (x, y, time) float64 5.68 9.256 0.7104 ... 7.992 4.615 7.805
    >>> ds
    <xarray.Dataset>
    Dimensions:         (time: 3, x: 2, y: 2)
    Coordinates:
        lon             (x, y) float64 -99.83 -99.32 -99.79 -99.23
        lat             (x, y) float64 42.25 42.21 42.63 42.59
      * time            (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08
        reference_time  datetime64[ns] 2014-09-05
    Dimensions without coordinates: x, y
    Data variables:
        temperature     (x, y, time) float64 29.11 18.2 22.83 ... 18.28 16.15 26.63
        precipitation   (x, y, time) float64 5.68 9.256 0.7104 ... 7.992 4.615 7.805
    """
    return 1

if name == 'main': import doctest doctest.testmod()

```

Is your feature request related to a problem? Please describe. As a new user I find it difficult to get a new dataset initialized because the necessary parameters are not shown in the docstring. I have to google "xarray dataset" to get to http://xarray.pydata.org/en/stable/generated/xarray.Dataset.html to figure it out.

In the figure below xarray.Dataset does not show the necessary parameters in the help pane:

Compare to pandas.DataFrame that includes it:

Describe the solution you'd like Looking at https://github.com/pydata/xarray/blob/master/xarray/core/dataset.py#L428 the xr.Dataset.__init__.__doc__ does contain the necessary parameters so the suggestion is to simply move or copy that information up one level toxr.Dataset.__doc__

For reference pandas does not use a docstring for the init method: https://github.com/pandas-dev/pandas/blob/v1.1.0/pandas/core/frame.py#L339-L9257

The pandas docs also includes a few simple copy/pasteable examples on how to initialize. So a xarray example would be: ```python

import numpy as np import xarray as xr x = np.arange(4) y = 2*x ds = xr.Dataset({'y': (['x'], y)}, ... coords={'x': x}) print(ds) <xarray.Dataset> Dimensions: (x: 4) Coordinates: * x (x) int32 0 1 2 3 Data variables: y (x) int32 0 2 4 6 ``` Or take some examples from http://xarray.pydata.org/en/stable/quick-overview.html#datasets or http://xarray.pydata.org/en/stable/data-structures.html#dataset although I found those a little bit confusing as they were dependent on previous results or rather complex with many dimensions.

Environment: