Add test and fix for categorical series .shift #10495.

doc/source/whatsnew/v0.17.0.txt

@@ -132,3 +132,4 @@ Bug Fixes
 
 - Bug in `pandas.concat` with ``axis=0`` when column is of dtype ``category`` (:issue:`10177`)
 - Bug in ``read_msgpack`` where input type is not always checked (:issue:`10369`)
+- Bug in ``Categorical`` ``Series.shift`` (:issue:`10495`)

pandas/core/internals.py

@@ -885,7 +885,13 @@ def shift(self, periods, axis=0):
         """ shift the block by periods, possibly upcast """
         # convert integer to float if necessary. need to do a lot more than
         # that, handle boolean etc also
-        new_values, fill_value = com._maybe_upcast(self.values)
+        if isinstance(self.values, Categorical):
+            # hack toward fixing issue 10495
+            values = self.values._codes
+        else:
+            values = self.values
+        new_values, fill_value = com._maybe_upcast(values)
+
         # make sure array sent to np.roll is c_contiguous
         f_ordered = new_values.flags.f_contiguous
         if f_ordered:
@@ -906,6 +912,13 @@ def shift(self, periods, axis=0):
         if f_ordered:
             new_values = new_values.T
 
+        if isinstance(self.values, Categorical):
+            # hack toward fixing issue 10495
+            new_values[np.isnan(new_values)] = -1
+            new_values = Categorical.from_codes(new_values,
+                    categories=self.values.categories)
+
+
         return [make_block(new_values,
                            ndim=self.ndim, fastpath=True,
                            placement=self.mgr_locs)]

pandas/tests/test_categorical.py

@@ -1175,6 +1175,18 @@ def test_comparison_with_unknown_scalars(self):
         self.assert_numpy_array_equal(cat == 4 , [False, False, False])
         self.assert_numpy_array_equal(cat != 4 , [True, True, True])
 
+    def test_shift(self):
+        # GH10495
+        # Series.shift should not depend on the dtype being categorical or not
+        values = ['a', 'b', 'c']
+        shifts = [-1, 0, 1]
+        results = [['b', 'c', np.nan], ['a', 'b', 'c'], [np.nan, 'a', 'b']]
+
+        for shift, result in zip(shifts, results):
+            b = pd.Series(pd.Categorical(result, categories=values))
+            a = pd.Series(values, dtype='category').shift(shift)
+            self.assert_series_equal(a, b)
+
 
 class TestCategoricalAsBlock(tm.TestCase):
     _multiprocess_can_split_ = True