ENH: DataFrame.pivot accepts a list of values

doc/source/whatsnew/v0.22.0.txt

@@ -240,4 +240,4 @@ With conda, use
 
 Note that the inconsistency in the return value for all-*NA* series is still
 there for pandas 0.20.3 and earlier. Avoiding pandas 0.21 will only help with
-the empty case.
+the empty case.

doc/source/whatsnew/v0.23.0.txt

@@ -145,6 +145,7 @@ Other Enhancements
 - ``Resampler`` objects now have a functioning :attr:`~pandas.core.resample.Resampler.pipe` method.
   Previously, calls to ``pipe`` were diverted to  the ``mean`` method (:issue:`17905`).
 - :func:`~pandas.api.types.is_scalar` now returns ``True`` for ``DateOffset`` objects (:issue:`18943`).
+- :func:`DataFrame.pivot` now accepts a list for the ``values=`` kwarg (:issue:`17160`).
 
 .. _whatsnew_0230.api_breaking:
 

pandas/core/frame.py

@@ -4355,8 +4355,8 @@ def pivot(self, index=None, columns=None, values=None):
             existing index.
         columns : string or object
             Column name to use to make new frame's columns
-        values : string or object, optional
-            Column name to use for populating new frame's values. If not
+        values : string, object or (0.23.0) a list of the previous, optional
+            Column name(s) to use for populating new frame's values. If not
             specified, all remaining columns will be used and the result will
             have hierarchically indexed columns
 
@@ -4381,15 +4381,16 @@ def pivot(self, index=None, columns=None, values=None):
 
         >>> df = pd.DataFrame({'foo': ['one','one','one','two','two','two'],
                                'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
-                               'baz': [1, 2, 3, 4, 5, 6]})
+                               'baz': [1, 2, 3, 4, 5, 6],
+                               'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
         >>> df
-            foo   bar  baz
-        0   one   A    1
-        1   one   B    2
-        2   one   C    3
-        3   two   A    4
-        4   two   B    5
-        5   two   C    6
+            foo   bar  baz  zoo
+        0   one   A    1    x
+        1   one   B    2    y
+        2   one   C    3    z
+        3   two   A    4    q
+        4   two   B    5    w
+        5   two   C    6    t
 
         >>> df.pivot(index='foo', columns='bar', values='baz')
              A   B   C
@@ -4401,6 +4402,15 @@ def pivot(self, index=None, columns=None, values=None):
         one  1   2   3
         two  4   5   6
 
+        >>> df.pivot(index='foo', columns='bar', values=['baz'])
+             A   B   C
+        one  1   2   3
+        two  4   5   6
+
+       >>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
+              A  B  C   A  B  C
+        one   1  2  3   x  y  z
+        two   4  5  6   q  w  t
 
         """
         from pandas.core.reshape.reshape import pivot

pandas/core/reshape/reshape.py

@@ -368,15 +368,18 @@ def pivot(self, index=None, columns=None, values=None):
         cols = [columns] if index is None else [index, columns]
         append = index is None
         indexed = self.set_index(cols, append=append)
-        return indexed.unstack(columns)
     else:
-        if index is None:
-            index = self.index
+        index = self.index if index is None else self[index]
+        index = MultiIndex.from_arrays([index, self[columns]])
+        if is_list_like(values):
+            # use DF in case of Iterable values (e.g: list, Series, np.array)
+            indexed = DataFrame(self[values].values,
+                                index=index,
+                                columns=values)
         else:
-            index = self[index]
-        indexed = Series(self[values].values,
-                         index=MultiIndex.from_arrays([index, self[columns]]))
-        return indexed.unstack(columns)
+            indexed = Series(self[values].values,
+                             index=index)
+    return indexed.unstack(columns)
 
 
 def pivot_simple(index, columns, values):

pandas/tests/reshape/test_pivot.py

@@ -371,6 +371,33 @@ def test_pivot_periods(self):
         pv = df.pivot(index='p1', columns='p2', values='data1')
         tm.assert_frame_equal(pv, expected)
 
+    @pytest.mark.parametrize('values', [
+        ['bar', 'baz'], np.array(['bar', 'baz']),
+        pd.Series(['bar', 'baz']), pd.Index(['bar', 'baz'])
+    ])
+    def test_pivot_with_list_like_values(self, values):
+        # issue #17160
+        df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
+                           'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
+                           'baz': [1, 2, 3, 4, 5, 6],
+                           'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
+
+        result = df.pivot(index='zoo', columns='foo', values=values)
+
+        data = [[np.nan, 'A', np.nan, 4],
+                [np.nan, 'C', np.nan, 6],
+                [np.nan, 'B', np.nan, 5],
+                ['A', np.nan, 1, np.nan],
+                ['B', np.nan, 2, np.nan],
+                ['C', np.nan, 3, np.nan]]
+        index = Index(data=['q', 't', 'w', 'x', 'y', 'z'], name='zoo')
+        columns = MultiIndex(levels=[['bar', 'baz'], ['one', 'two']],
+                             labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
+                             names=[None, 'foo'])
+        expected = DataFrame(data=data, index=index,
+                             columns=columns, dtype='object')
+        tm.assert_frame_equal(result, expected)
+
     def test_margins(self):
         def _check_output(result, values_col, index=['A', 'B'],
                           columns=['C'],