Optimize min_count for all numpy

For pure numpy arrays, min_count=1 (xarray default) is the same
as min_count=None, with the right fill_value. This avoids
one useless pass over the data, and one useless copy.

We need to always accumulate count with dask, to make sure we
get the right values at the end.

flox/core.py

@@ -2486,6 +2486,12 @@ def groupby_reduce(
         return (result, groups)
 
     elif not has_dask:
+        if min_count_ == 1:
+            # optimize for pure numpy groupby
+            # We set the fill_value appropriately anyway
+            agg.min_count = None
+            agg.numpy = agg.numpy[:-1]
+
         results = _reduce_blockwise(
             array, by_, agg, expected_groups=expected_, reindex=reindex, sort=sort, **kwargs
         )