BUG: pivot_table strings as aggfunc

doc/source/whatsnew/v0.23.0

@@ -341,7 +341,8 @@ Reshaping
 - Bug in :func:`DataFrame.stack` which fails trying to sort mixed type levels under Python 3 (:issue:`18310`)
 - Fixed construction of a :class:`Series` from a ``dict`` containing ``NaN`` as key (:issue:`18480`)
 - Bug in :func:`Series.rank` where ``Series`` containing ``NaT`` modifies the ``Series`` inplace (:issue:`18521`)
--
+- Bug in :func:`Dataframe.pivot_table` which fails when the ``aggfunc`` arg is of type string.  The behavior is now consistent with other methods like ``agg`` and ``apply`` (:issue:`18713`)
+- 
 
 Numeric
 ^^^^^^^

pandas/core/reshape/pivot.py

@@ -38,7 +38,8 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
                                 fill_value=fill_value, aggfunc=func,
                                 margins=margins, margins_name=margins_name)
             pieces.append(table)
-            keys.append(func.__name__)
+            keys.append(getattr(func, '__name__', func))
+
         return concat(pieces, keys=keys, axis=1)
 
     keys = index + columns

pandas/tests/reshape/test_pivot.py

@@ -1109,6 +1109,51 @@ def test_pivot_margins_name_unicode(self):
         expected = pd.DataFrame(index=index)
         tm.assert_frame_equal(table, expected)
 
+    def test_pivot_string_as_func(self):
+        # GH #18713
+        # for correctness purposes
+        data = DataFrame({'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar',
+                                'bar', 'bar', 'foo', 'foo', 'foo'],
+                          'B': ['one', 'one', 'one', 'two', 'one', 'one',
+                                'one', 'two', 'two', 'two', 'one'],
+                          'C': range(11)})
+
+        result = pivot_table(data, index='A', columns='B', aggfunc='sum')
+        mi = MultiIndex(levels=[['C'], ['one', 'two']],
+                        labels=[[0, 0], [0, 1]], names=[None, 'B'])
+        expected = DataFrame({('C', 'one'): {'bar': 15, 'foo': 13},
+                              ('C', 'two'): {'bar': 7, 'foo': 20}},
+                             columns=mi).rename_axis('A')
+        tm.assert_frame_equal(result, expected)
+
+        result = pivot_table(data, index='A', columns='B',
+                             aggfunc=['sum', 'mean'])
+        mi = MultiIndex(levels=[['sum', 'mean'], ['C'], ['one', 'two']],
+                        labels=[[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 1]],
+                        names=[None, None, 'B'])
+        expected = DataFrame({('mean', 'C', 'one'): {'bar': 5.0, 'foo': 3.25},
+                              ('mean', 'C', 'two'): {'bar': 7.0,
+                                                     'foo': 6.666666666666667},
+                              ('sum', 'C', 'one'): {'bar': 15, 'foo': 13},
+                              ('sum', 'C', 'two'): {'bar': 7, 'foo': 20}},
+                             columns=mi).rename_axis('A')
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize('f, f_numpy',
+                             [('sum', np.sum),
+                              ('mean', np.mean),
+                              ('std', np.std),
+                              (['sum', 'mean'], [np.sum, np.mean]),
+                              (['sum', 'std'], [np.sum, np.std]),
+                              (['std', 'mean'], [np.std, np.mean])])
+    def test_pivot_string_func_vs_func(self, f, f_numpy):
+        # GH #18713
+        # for consistency purposes
+        result = pivot_table(self.data, index='A', columns='B', aggfunc=f)
+        expected = pivot_table(self.data, index='A', columns='B',
+                               aggfunc=f_numpy)
+        tm.assert_frame_equal(result, expected)
+
 
 class TestCrosstab(object):