diff --git a/doc/user-guide/groupby.rst b/doc/user-guide/groupby.rst
index 6c7834cfc90..44b6f99f083 100644
--- a/doc/user-guide/groupby.rst
+++ b/doc/user-guide/groupby.rst
@@ -241,6 +241,8 @@ applying your function, and then unstacking the result:
 Extending GroupBy: Grouper Objects
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. currentmodule:: xarray.core.groupers
+
 The first step in executing a GroupBy analysis is to *identify* the groups and create an intermediate array where each group member is identified
 by a unique integer code. Commonly this step is executed using :py:func:`pandas.factorize` for grouping by a categorical variable (e.g. ``['a', 'b', 'a', 'b']``)
 and :py:func:`pandas.cut` or :py:func:`numpy.digitize` or :py:func:`numpy.searchsorted` for binning a numeric variable.
@@ -252,9 +254,9 @@ Eventually, they will also provide a natural way to extend GroupBy to grouping b
 
 Xarray provides three Grouper objects today
 
-1. :py:class:`groupers.UniqueGrouper` for categorical grouping
-2. :py:class:`groupers.BinGrouper` for binned grouping
-3. :py:class:`groupers.TimeResampler` for resampling along a datetime coordinate
+1. :py:class:`UniqueGrouper` for categorical grouping
+2. :py:class:`BinGrouper` for binned grouping
+3. :py:class:`TimeResampler` for resampling along a datetime coordinate
 
 These objects mean that
 
@@ -276,14 +278,9 @@ an instance of :py:class:`EncodedGroups`.
 
 .. ipython:: python
 
-    import numpy as np
-    from xarray.groupers import Grouper, EncodedGroups
-
-
-    class YearGrouper(Grouper):
-        def factorize(self, group) -> EncodedGroups:
+    class YearGrouper(xr.groupers.Grouper):
+        def factorize(self, group) -> xr.groupers.EncodedGroups:
             assert np.issubdtype(group.dtype, np.datetime64)
             year = group.dt.year
             codes, uniques = pd.factorize(year)
-
             return EncodedGroups(codes=codes)