BUG: pivot_table someitmes returns Series (pandas-dev#4386)

BUG: pivot_table sometimes returns Series (pandas-dev#4386)

BUG: pivot_table sometimes returns Series (pandas-dev#4386)

Author: mroeschke

doc/source/whatsnew/v0.19.1.txt

@@ -82,4 +82,5 @@ Bug Fixes
 
 
 - Bug in ``pd.pivot_table`` may raise ``TypeError`` or ``ValueError`` when ``index`` or ``columns``
-  is not scalar and ``values`` is not specified (:issue:`14380`)
+  is not scalar and ``values`` is not specified (:issue:`14380`)
+- Bug in ``pd.pivot_table`` where a ``Series`` is return instead of a ``DataFrame`` (:issue:`4386`)

pandas/tests/test_categorical.py

@@ -3120,9 +3120,9 @@ def test_pivot_table(self):
             [Categorical(["a", "b", "z"], ordered=True),
              Categorical(["c", "d", "y"], ordered=True)],
             names=['A', 'B'])
-        expected = Series([1, 2, np.nan, 3, 4, np.nan, np.nan, np.nan, np.nan],
-                          index=exp_index, name='values')
-        tm.assert_series_equal(result, expected)
+        exp_data = [1, 2, np.nan, 3, 4, np.nan, np.nan, np.nan, np.nan]
+        expected = DataFrame(exp_data, index=exp_index, columns=['values'])
+        tm.assert_frame_equal(result, expected)
 
     def test_count(self):
 

pandas/tools/pivot.py

@@ -78,7 +78,6 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
     """
     index = _convert_by(index)
     columns = _convert_by(columns)
-
     if isinstance(aggfunc, list):
         pieces = []
         keys = []
@@ -164,12 +163,16 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
                              margins_name=margins_name)
 
     # discard the top level
-    if values_passed and not values_multi and not table.empty:
+    if values_passed and not values_multi and not table.empty and \
+        (table.columns.nlevels > 1):
         table = table[values[0]]
 
     if len(index) == 0 and len(columns) > 0:
         table = table.T
 
+    if isinstance(table, Series):
+        table = table.to_frame()
+
     return table
 
 

pandas/tools/tests/test_pivot.py

@@ -350,13 +350,14 @@ def _check_output(result, values_col, index=['A', 'B'],
         # no rows
         rtable = self.data.pivot_table(columns=['AA', 'BB'], margins=True,
                                        aggfunc=np.mean)
-        tm.assertIsInstance(rtable, Series)
+        expected = self.data.groupby(['AA','BB']).mean()
+        expected.loc[('All', ''), :] = self.data[['DD', 'EE', 'FF']].mean()
+        expected = (expected.stack()
+                            .unstack(['BB', 'AA'])
+                            .stack(['AA', 'BB'])
+                            .to_frame())#this still returns a series
 
-        table = self.data.pivot_table(index=['AA', 'BB'], margins=True,
-                                      aggfunc='mean')
-        for item in ['DD', 'EE', 'FF']:
-            totals = table.loc[('All', ''), item]
-            self.assertEqual(totals, self.data[item].mean())
+        tm.assert_frame_equal(expected, rtable)
 
         # issue number #8349: pivot_table with margins and dictionary aggfunc
         data = [
@@ -485,8 +486,11 @@ def test_margins_no_values_no_cols(self):
         # Regression test on pivot table: no values or cols passed.
         result = self.data[['A', 'B']].pivot_table(
             index=['A', 'B'], aggfunc=len, margins=True)
-        result_list = result.tolist()
-        self.assertEqual(sum(result_list[:-1]), result_list[-1])
+        expected = self.data[['A', 'B']].groupby(['A', 'B']).apply(len)
+        expected.loc[('All', '')] = expected.sum()
+        expected = expected.to_frame()
+
+        tm.assert_frame_equal(result, expected) #need to return dataframe
 
     def test_margins_no_values_two_rows(self):
         # Regression test on pivot table: no values passed but rows are a
@@ -854,6 +858,39 @@ def test_categorical_margins(self):
         table = data.pivot_table('x', 'y', 'z', margins=True)
         tm.assert_frame_equal(table, expected)
 
+    def test_always_return_dataframe(self):
+        # GH 4386
+        df = DataFrame({'col1': [3, 4, 5],
+                        'col2': ['C', 'D', 'E'],
+                        'col3': [1, 3, 9]})
+        result = df.pivot_table('col1', index=['col3', 'col2'], aggfunc=np.sum)
+        m = MultiIndex.from_arrays([[1, 3, 9],
+                                    ['C', 'D', 'E']],
+                                   names=['col3', 'col2'])
+        expected = DataFrame([3, 4, 5],
+                             index=m, columns=['col1'])
+
+        tm.assert_frame_equal(result, expected)
+
+        result = df.pivot_table(
+            'col1', index='col3', columns='col2', aggfunc=np.sum
+        )
+        expected = DataFrame([[3, np.NaN, np.NaN],
+                              [np.NaN, 4, np.NaN],
+                              [np.NaN, np.NaN, 5]],
+                             index=Index([1, 3, 9], name='col3'),
+                             columns=Index(['C', 'D', 'E'], name='col2'))
+
+        tm.assert_frame_equal(result, expected)
+
+        result = df.pivot_table('col1', index='col3', aggfunc=[np.sum])
+        m = MultiIndex.from_arrays([['sum'],
+                                    ['col1']])
+        expected = DataFrame([3, 4, 5],
+                             index=Index([1, 3, 9], name='col3'),
+                             columns=m)
+        tm.assert_frame_equal(result, expected)
+
 
 class TestCrosstab(tm.TestCase):