DOC: Clarify groupby axis operations

doc/source/user_guide/groupby.rst

@@ -137,8 +137,9 @@ 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
-a transpose:
+The above GroupBy will split the DataFrame on its index (rows). DataFrame groupby
+always operates along axis 0 (rows). To split by columns instead, first transpose
+the DataFrame:
 
 .. ipython::
 
@@ -151,6 +152,11 @@ a transpose:
 
     In [5]: grouped = df.T.groupby(get_letter_type)
 
+.. note::
+
+   Prior to pandas 3.0, groupby had an ``axis`` parameter. This has been removed.
+   To group by columns, transpose your DataFrame using ``.T`` before calling groupby.
+
 pandas :class:`~pandas.Index` objects support duplicate values. If a
 non-unique index is used as the group key in a groupby operation, all values
 for the same index value will be considered to be in one group and thus the

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``.