package `xarray` and `xarray-core` in conda-forge

What is your issue?

The current set of Xarray dependencies is very minimal.
https://github.com/pydata/xarray/blob/3fd162e42bb309cfab03c2c18b037d1ad3cd3193/pyproject.toml#L25-L29

This is pretty unfriendly to a new user, and not a great out-of-the-box experience. You can't read any files (except npz, csv, parquet I guess), you can't access any tutorial datasets, you can't make plots, and you're missing a bunch of effectively free performance optimizations.

I think the current set of minimal dependencies is more appropriate to an xarray-core package.
Here are our optional dependencies for example:
https://github.com/pydata/xarray/blob/3fd162e42bb309cfab03c2c18b037d1ad3cd3193/pyproject.toml#L31-L48

Proposal

I suggest that we migrate to xarray-core and xarray packages in conda-forge.:

1. xarray-core will have the current set of minimal dependencies.
2. For xarray I propose the following dependencies:
   1. flox, opt_einsum, numbagg for accelerated computations
   2. fsspec, netcdf, zarr for reading common datasets & "cloud"
   3. matplotlib for plotting.
   4. pooch to read tutorial datasets

Related: dask packages dask-core and I think matplotlib packages matplotlib-base

Start by reviewing the linked dependency sections in pyproject.toml and the existing conda-forge packaging for xarray. Compare the current minimal dependencies with the proposed xarray-core and xarray sets, including flox, opt_einsum, numbagg, fsspec, netcdf, zarr, matplotlib, and pooch. Done means the two packages are defined with the intended dependency split and can be installed successfully.