DOC: Add details of dropna in DataFrame.pivot_table

pandas/core/frame.py

@@ -9347,8 +9347,12 @@ def pivot(
             on the rows and columns.
         dropna : bool, default True
             Do not include columns whose entries are all NaN. If True,
-            rows with a NaN value in any column will be omitted before
-            computing margins.
+
+            * rows with an NA value in any column will be omitted before computing
+              margins,
+            * index/column keys containing NA values will be dropped (see ``dropna``
+              parameter in :meth:`DataFrame.groupby`).
+
         margins_name : str, default 'All'
             Name of the row / column that will contain the totals
             when margins is True.

pandas/core/reshape/pivot.py

@@ -102,8 +102,11 @@ def pivot_table(
         on the rows and columns.
     dropna : bool, default True
         Do not include columns whose entries are all NaN. If True,
-        rows with a NaN value in any column will be omitted before
-        computing margins.
+
+        * rows with an NA value in any column will be omitted before computing margins,
+        * index/column keys containing NA values will be dropped (see ``dropna``
+          parameter in :meth:``DataFrame.groupby``).
+
     margins_name : str, default 'All'
         Name of the row / column that will contain the totals
         when margins is True.

pandas/tests/reshape/test_pivot.py

@@ -2529,6 +2529,30 @@ def test_pivot_table_aggfunc_nunique_with_different_values(self):
 
         tm.assert_frame_equal(result, expected)
 
+    def test_pivot_table_index_and_column_keys_with_nan(self, dropna):
+        # GH#61113
+        data = {"row": [None, *range(4)], "col": [*range(4), None], "val": range(5)}
+        df = DataFrame(data)
+        result = df.pivot_table(values="val", index="row", columns="col", dropna=dropna)
+        e_axis = [*range(4), None]
+        nan = np.nan
+        e_data = [
+            [nan, 1.0, nan, nan, nan],
+            [nan, nan, 2.0, nan, nan],
+            [nan, nan, nan, 3.0, nan],
+            [nan, nan, nan, nan, 4.0],
+            [0.0, nan, nan, nan, nan],
+        ]
+        expected = DataFrame(
+            data=e_data,
+            index=Index(data=e_axis, name="row"),
+            columns=Index(data=e_axis, name="col"),
+        )
+        if dropna:
+            expected = expected.loc[[0, 1, 2], [1, 2, 3]]
+
+        tm.assert_frame_equal(left=result, right=expected)
+
 
 class TestPivot:
     def test_pivot(self):