BUG: pivot_table not returning correct type when margin=True and aggf…

…unc='mean' (pandas-dev#28248)

doc/source/whatsnew/v1.0.0.rst

@@ -595,6 +595,7 @@ Reshaping
 
 - Bug in :meth:`DataFrame.apply` that caused incorrect output with empty :class:`DataFrame` (:issue:`28202`, :issue:`21959`)
 - Bug in :meth:`DataFrame.stack` not handling non-unique indexes correctly when creating MultiIndex (:issue: `28301`)
+- Bug in :meth:`pivot_table` not returning correct type ``float`` when ``margins=True`` and ``aggfunc='mean'`` (:issue:`24893`)
 - Bug :func:`merge_asof` could not use :class:`datetime.timedelta` for ``tolerance`` kwarg (:issue:`28098`)
 - Bug in :func:`merge`, did not append suffixes correctly with MultiIndex (:issue:`28518`)
 - :func:`qcut` and :func:`cut` now handle boolean input (:issue:`20303`)
@@ -604,6 +605,7 @@ Reshaping
 - Bug where :meth:`DataFrame.equals` returned True incorrectly in some cases when two DataFrames had the same columns in different orders (:issue:`28839`)
 - Bug in :meth:`DataFrame.replace` that caused non-numeric replacer's dtype not respected (:issue:`26632`)
 
+
 Sparse
 ^^^^^^
 - Bug in :class:`SparseDataFrame` arithmetic operations incorrectly casting inputs to float (:issue:`28107`)

pandas/core/reshape/pivot.py

@@ -261,9 +261,12 @@ def _add_margins(
 
     row_names = result.index.names
     try:
+        # check the result column and leave floats
         for dtype in set(result.dtypes):
             cols = result.select_dtypes([dtype]).columns
-            margin_dummy[cols] = margin_dummy[cols].astype(dtype)
+            margin_dummy[cols] = margin_dummy[cols].apply(
+                maybe_downcast_to_dtype, args=(dtype,)
+            )
         result = result.append(margin_dummy)
     except TypeError:
 

pandas/tests/reshape/test_pivot.py

@@ -1656,6 +1656,24 @@ def test_categorical_margins_category(self, observed):
         table = df.pivot_table("x", "y", "z", dropna=observed, margins=True)
         tm.assert_frame_equal(table, expected)
 
+    def test_margins_casted_to_float(self, observed):
+        # GH 24893
+        df = pd.DataFrame(
+            {
+                "A": [2, 4, 6, 8],
+                "B": [1, 4, 5, 8],
+                "C": [1, 3, 4, 6],
+                "D": ["X", "X", "Y", "Y"],
+            }
+        )
+
+        result = pd.pivot_table(df, index="D", margins=True)
+        expected = pd.DataFrame(
+            {"A": [3, 7, 5], "B": [2.5, 6.5, 4.5], "C": [2, 5, 3.5]},
+            index=pd.Index(["X", "Y", "All"], name="D"),
+        )
+        tm.assert_frame_equal(result, expected)
+
     def test_pivot_with_categorical(self, observed, ordered_fixture):
         # gh-21370
         idx = [np.nan, "low", "high", "low", np.nan]