What

Brings LinearExpression.groupby to parity with xarray.Dataset.groupby for grouping by coordinate names — on the fast path, with no breaking change. Two related pieces:

1. Group by an attached non-dimension coordinate (closes #750).

expr = (1 * x).assign_coords(period=period)   # 'period' rides on the 'snapshot' dim
expr.groupby("period").sum()   # before: ValueError: period already exists as coordinate
expr.groupby(period).sum()     # before: KeyError: 'period'

Both now work and take the fast path, mirroring xarray. (Grouping by a dimension name always worked — only attached non-dim coordinates were broken.) The old workaround was to detach the coord first (expr.drop_vars("period").groupby(period).sum()).

2. Multi-key grouping by names now takes the fast path (closes #753).

expr.groupby(["period", "season"]).sum()   # before: silently dropped to the slow xarray fallback

It now rides the reindex fast path and returns the same output — one dimension per key, byte-identical to the fallback, sparse fill cells included. The pd.DataFrame grouper is untouched and keeps its compact stacked-MultiIndex output, so this is non-breaking.

How

In LinearExpressionGroupby:

• _resolve_group normalizes a key: unwrap a single-element list (groupby(["period"]) → scalar), and resolve a string coord name to its coordinate so it takes the fast path.
• sum() drops every coordinate aligned to the grouped dimension before reshaping, so an attached aux coord (including the one being grouped by) no longer collides on the final rename.
• The groupby property detaches a free (non-indexed) coordinate before handing it to xarray — fixing the use_fallback=True path — but never a MultiIndex level.
• A list of coord names (1-D, sharing one dim) is gathered into a value frame to ride the fast path, then unstacked back into one dimension per key.

Memory

One dimension per key is a dense cartesian grid, so a sparse key crossing materialises mostly-fill cells (measured ~100× vs the compact DataFrame grouper for a diagonal crossing — see #740). A UserWarning nudges sparse/high-cardinality users to the DataFrame grouper; it reads the collapsed MultiIndex levels, so it is O(observed), not O(N), and fires before unstack allocates the grid. Getting separate dims and compact storage would need a sparse/long-format kernel — tracked in #757 (the groupby-densification follow-up) under the sparse-kernel umbrella #756.

Tests

TestGroupbyByAttachedCoordinate asserts grouped vars/coeffs against hard-coded results on a deterministic model: single-key (name/DataArray × use_fallback), 2-D variable, dimension-coordinate-by-name, single-element list, MultiIndex level, and a pytest.raises row pinning that a list of DataArrays is unsupported. TestMultiKeyFastPath covers the multi-key fast path: fast == fallback (list/tuple, including a sparse crossing), separate-dims-not-stacked, sparse-combination-filled, DataFrame-grouper-stays-compact, and the blow-up warning (fires when sparse, silent when dense). Full suite green; ruff + mypy clean.

Closes #750, #753. Drafted with an agent (Claude Code).

🤖 Generated with Claude Code