This allows adding a dimension of an array. Which is relevant when for example using keepdims in NamedArray.reduce.

Normally you can do this with None but that's not really allowed in the getitem documentation currently https://data-apis.org/array-api/2022.12/API_specification/generated/array_api.array.getitem.html

Should follow the standard but with an additional dim argument, dim should be preferred over the axis argument. https://data-apis.org/array-api/draft/API_specification/generated/array_api.expand_dims.html

xref #8344, #8406

It can be helpful to get a second opinion from another typer checker than mypy.

The CI activates when the run-pyright label is added.

• [x] Closes #7314

• [x] Closes #7268

• Matplotlib adds default colors with plt.plot(x, y) when y is a 2D array.

• Matplotlib prioritizes c over default colors with plt.plot(x, y, c="blue") when y is a 2D array.
• Seaborn prioritizes hue over c, but all plot axes has to be explicit.

The plot1d function now follows closer to seaborns example and require explicitly defining optional dimensions such as hue/markersize. Positives and negatives: +++ Reliable performance, won't accidentally choose large and heavy arrays to plot. - Loss of dimension information for default values, will essentially flatten the n-dimensional arrays. - Other xarray plot functions guesses quite a bit as well, API is not consistent.

Required dimensions like the x-axis will still be guessed since the mpl errors weren't very intuitive.

While at it: * Make it possible to prioritize certain mpl kwargs when hue/markersize isn't defined. * Fix integer coords in plot1d and facetgrid, use .coords instead of getitem.

Activate typing on these mixins by removing the Any.

• [ ] 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

• Rely on array backend for string formatting instead of forcing to numpy arrays. The previous implementation used .values to force to numpy arrays which doesn't work for sparse arrays and might be scary in the case of large dask arrays.

• Only allow format_spec when dealing with scalar arrays, shape=(). Using f-strings is a common method to print the repr. If we want to have full support of .__format__ we should probably add format_spec as input to formatting.array_repr.

• [x] Closes #6822

• [x] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

```python import pandas as pd import xarray as xr

s = pd.Series( range(4), index=pd.MultiIndex.from_product([list("ab"), list("cd")]), )

da = xr.DataArray.from_series(s, sparse=True)

Handle sparse:

print(f"Error: {da} is sparse") Error: <xarray.DataArray (level_0: 2, level_1: 2)> <COO: shape=(2, 2), dtype=float64, nnz=4, fill_value=nan> Coordinates: * level_0 (level_0) object 'a' 'b' * level_1 (level_1) object 'c' 'd' is sparse

Handle format_spec:

da = xr.DataArray([1, 2, 3])

print(f'{da[0]}') <xarray.DataArray ()> array(1)

print(f'{da[0]:d}') 1

print(f'{da[0]:f}') 1.000000

da = xr.DataArray([1, 2, 3]) print(f'{da:.f}') Traceback (most recent call last): (...) NotImplementedError: Using format_spec is only supported when shape is (). Got shape = (3,). ```

Noticed these issues when trying to add typing to some helper functions in #6778.

Follow up from #6637.