unstack() should be smart enough to recognise that all labels in a coord are strings, and convert them to numpy strings. This is particularly relevant e.g. if you want to dump the xarray to netcdf and then read it with a non-python library.

``` python import xarray

a = xarray.DataArray([[1,2],[3,4]], dims=['x', 'y'], coords={'x': ['x1', 'x2'], 'y': ['y1', 'y2']}) a ```

<xarray.DataArray (x: 2, y: 2)> array([[1, 2], [3, 4]]) Coordinates: * y (y) <U2 'y1' 'y2' * x (x) <U2 'x1' 'x2'

python a.stack(s=['x', 'y']).unstack('s')

<xarray.DataArray (x: 2, y: 2)> array([[1, 2], [3, 4]]) Coordinates: * x (x) object 'x1' 'x2' * y (y) object 'y1' 'y2'

The documentation for set_options laconically says:

arithmetic_join: DataArray/Dataset alignment in binary operations. Default: 'inner'.

leaving the user wonder what the other options are. Also, the set_options code does not make any kind of domain check on the possible values. By scanning the code I gathered that the valid values (and their meanings) should be the same as align(join=...), but I'd like confirmation on that...

Closes #4295

Increase width of the sliding window for minimum supported version: - setuptools from 6 months sliding window to hardcoded >= 38.4, and to 42 months sliding window starting from July 2021 - dask and distributed from 6 months sliding window to hardcoded >= 2.9, and to 12 months sliding window starting from January 2021 - all other libraries from 6 months to 12 months sliding window

Yet another major improvement for #2799.

Achieve a 2x to 5x boost in isel performance when slicing small arrays by int, slice, list of int, scalar ndarray, or 1-dimensional ndarray.

```python import xarray

da = xarray.DataArray([[1, 2], [3, 4]], dims=['x', 'y']) v = da.variable a = da.variable.values ds = da.to_dataset(name="d")

ds_with_idx = xarray.Dataset({ 'x': [10, 20], 'y': [100, 200], 'd': (('x', 'y'), [[1, 2], [3, 4]]) }) da_with_idx = ds_with_idx.d

before -> after

%timeit a[0] # 121 ns %timeit v[0] # 7 µs %timeit v.isel(x=0) # 10 µs %timeit da[0] # 65 µs -> 15 µs %timeit da.isel(x=0) # 63 µs -> 13 µs %timeit ds.isel(x=0) # 48 µs -> 24 µs %timeit da_with_idx[0] # 209 µs -> 82 µs %timeit da_with_idx.isel(x=0, drop=False) # 135 µs -> 34 µs %timeit da_with_idx.isel(x=0, drop=True) # 101 µs -> 34 µs %timeit ds_with_idx.isel(x=0, drop=False) # 90 µs -> 49 µs %timeit ds_with_idx.isel(x=0, drop=True) # 65 µs -> 49 µs ```

Marked as WIP because this commands running the asv suite to verify there are no regressions for large arrays. (on a separate note, we really need to add the small size cases to asv - as discussed in #3382).

This profoundly alters one of the most important methods in xarray and I must confess it makes me nervous, particularly as I am unsure if the test coverage of DataArray.isel() is as through as that for Dataset.isel().

Propaedeutic CI work to #3222.

• py36 and py37 are now identical
• Many optional dependencies were missing in one test suite or another (see details below)
• Tests that require hypothesis now always run if hypothesis is installed
• py37-windows.yml requirements file has been rebuilt starting from py37.yml
• Sorted requirements files alphabetically for better maintainability
• Added black. This is not needed by CI, but I personally use these yaml files to deploy my dev environment and I would expect many more developers to do the same. Alternatively, we could go the other way around and remove flake8 from everywhere and mypy from py36 and py37-windows. IMHO the marginal speedup would not be worth the complication.

Added packages to py36.yml (net of changes in order): + black + hypothesis + nc-time-axis + numba + numbagg + pynio (https://github.com/pydata/xarray/issues/3154 seems to be now fixed upstream) + sparse

Added packages to py37.yml (net of changes in order):

• black
• cdms2
• hypothesis
• iris>=1.10
• numba (previously implicitly installed from pip by numbagg; now installed from conda)
• pynio

Added packages to py37-windows.yml (net of changes in order):

• black
• bottleneck
• flake8
• hypothesis
• iris>=1.10
• lxml
• mypy==0.720
• numba
• numbagg
• pseudonetcdf>=3.0.1
• pydap
• sparse

• Clean up everywhere the pattern def my_func(*args, **kwargs): my_optional_arg = kwargs.pop('my_optional_arg', None) which was inherited from not being able to put named keyword arguments after *args in Python 2.

• Fix bug in SplineInterpolator where the __init__ method would write to the class attributes of BaseInterpolator.

• map_dataarray was unintentionally and subtly relying on _process_cmap_cbar_kwargs to modify the kwargs in place. _process_cmap_cbar_kwargs is now strictly read-only and the modifications in kwargs have been made explicit in the caller function.
• Rename all 'kwds' to 'kwargs' for sake of coherency

• [x] Closes #1521
• [x] Tests added / passed
• [x] Passes git diff upstream/master | flake8 --diff
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API