Update docs.

docs/source/engines.md

@@ -9,18 +9,20 @@
 1. `engine="numba"` wraps `numpy_groupies.aggregate_numba`. This uses `numba` kernels for the core aggregation.
 1. `engine="flox"` uses the `ufunc.reduceat` method after first argsorting the array so that all group members occur sequentially. This was copied from
    a [gist by Stephan Hoyer](https://gist.github.com/shoyer/f538ac78ae904c936844)
+1. `engine="numbagg"` uses the reductions available in [`numbagg.grouped`](https://github.com/numbagg/numbagg/blob/main/numbagg/grouped.py)
+   from the [numbagg](https://github.com/numbagg/numbagg) project.
 
 See [](arrays) for more details.
 
 ## Tradeoffs
 
-For the common case of reducing a nD array by a 1D array of group labels (e.g. `groupby("time.month")`), `engine="flox"` *can* be faster.
+For the common case of reducing a nD array by a 1D array of group labels (e.g. `groupby("time.month")`), `engine="numbagg"` is almost always faster, and `engine="flox"` *can* be faster.
 
 The reason is that `numpy_groupies` converts all groupby problems to a 1D problem, this can involve [some overhead](https://github.com/ml31415/numpy-groupies/pull/46).
 It is possible to optimize this a bit in `flox` or `numpy_groupies`, but the work has not been done yet.
 The advantage of `engine="numpy"` is that it tends to work for more array types, since it appears to be more common to implement `np.bincount`, and not `np.add.reduceat`.
 
 ```{tip}
-Other potential engines we could add are [`numbagg`](https://github.com/numbagg/numbagg) ([stalled PR here](https://github.com/xarray-contrib/flox/pull/72)) and [`datashader`](https://github.com/xarray-contrib/flox/issues/142).
-Both use numba for high-performance aggregations. Contributions or discussion is very welcome!
+One other potential engine we could add is [`datashader`](https://github.com/xarray-contrib/flox/issues/142).
+Contributions or discussion is very welcome!
 ```