diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
index 4ec34db6ed959..40369bd40cdb5 100644
--- a/doc/source/user_guide/groupby.rst
+++ b/doc/source/user_guide/groupby.rst
@@ -137,7 +137,7 @@ We could naturally group by either the ``A`` or ``B`` columns, or both:
 
    ``df.groupby('A')`` is just syntactic sugar for ``df.groupby(df['A'])``.
 
-The above GroupBy will split the DataFrame on its index (rows). To split by columns, first do
+DataFrame groupby always operates along axis 0 (rows). To split by columns, first do
 a transpose:
 
 .. ipython::
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 68ea6795d47dd..e629ea43da79a 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -9432,7 +9432,7 @@ def groupby(
             index. If a dict or Series is passed, the Series or dict VALUES
             will be used to determine the groups (the Series' values are first
             aligned; see ``.align()`` method). If a list or ndarray of length
-            equal to the selected axis is passed (see the `groupby user guide
+            equal to the number of rows is passed (see the `groupby user guide
             <https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups>`_),
             the values are used as-is to determine the groups. A label or list
             of labels may be passed to group by the columns in ``self``.