pandas/core/reshape/reshape.py

@@ -253,7 +253,7 @@ def get_new_columns(self):
             new_names = [self.value_columns.name, self.removed_name]
             new_labels = [propagator]
 
-        # The two indices differ iff the unstacked level had unused items.
+        # The two indices differ only if the unstacked level had unused items:
         if len(self.removed_level_full) != len(self.removed_level):
             # In this case, we remap the new labels to the original level:
             repeater = self.removed_level_full.get_indexer(self.removed_level)
@@ -262,6 +262,7 @@ def get_new_columns(self):
         else:
             # Otherwise, we just use each level item exactly once:
             repeater = np.arange(stride) - self.lift
+
         # The entire level is then just a repetition of the single chunk:
         new_labels.append(np.tile(repeater, width))
         return MultiIndex(levels=new_levels, labels=new_labels,

pandas/tests/frame/test_reshape.py

@@ -561,7 +561,7 @@ def test_unstack_dtypes(self):
             tm.assert_frame_equal(left, right)
 
     def test_unstack_unused_levels(self):
-        # GH 17845: sliced columns of int DataFrame
+        # GH 17845: unused labels in index make unstack() cast int to float
         idx = pd.MultiIndex.from_product([['a'], ['A', 'B', 'C', 'D']])[:-1]
         df = pd.DataFrame([[1, 0]] * 3, index=idx)
 
@@ -579,9 +579,10 @@ def test_unstack_unused_levels(self):
         block = np.arange(4).reshape(2, 2)
         df = pd.DataFrame(np.concatenate([block, block + 4]), index=idx)
         result = df.unstack()
-        expected = pd.DataFrame(np.concatenate([block * 2, block * 2 - 1],
+        expected = pd.DataFrame(np.concatenate([block * 2, block * 2 + 1],
                                                axis=1),
                                 columns=idx)
+        tm.assert_frame_equal(result, expected)
         assert((result.columns.levels[1] == idx.levels[1]).all())
 
         # With mixed dtype and NaN
@@ -625,7 +626,7 @@ def test_unstack_unused_level(self, cols):
         expected.columns = MultiIndex.from_product([expected.columns, ['I']],
                                                    names=[None, 'C'])
         expected.index = expected.index.droplevel('C')
-        assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result, expected)
 
     def test_unstack_nan_index(self):  # GH7466
         cast = lambda val: '{0:1}'.format('' if val != val else val)