Categorical: let unique only return used categories

doc/source/whatsnew/v0.15.2.txt

@@ -42,6 +42,9 @@ API changes
 
 - Bug in concat of Series with ``category`` dtype which were coercing to ``object``. (:issue:`8641`)
 
+- Bug in unique of Series with ``category`` dtype, which returned all categories regardless
+  whether they were "used" or not (see :issue:`8559` for the discussion).
+
 - ``Series.all`` and ``Series.any`` now support the ``level`` and ``skipna`` parameters. ``Series.all``, ``Series.any``, ``Index.all``, and ``Index.any`` no longer support the ``out`` and ``keepdims`` parameters, which existed for compatibility with ndarray. Various index types no longer support the ``all`` and ``any`` aggregation functions and will now raise ``TypeError``. (:issue:`8302`):
 
   .. ipython:: python

pandas/core/categorical.py

@@ -1326,13 +1326,18 @@ def unique(self):
         """
         Return the unique values.
 
-        This includes all categories, even if one or more is unused.
+        Unused categories are NOT returned.
 
         Returns
         -------
         unique values : array
         """
-        return np.asarray(self.categories)
+        unique_codes = np.unique(self.codes)
+        # for compatibility with normal unique, which has nan last
+        if unique_codes[0] == -1:
+            unique_codes[0:-1] = unique_codes[1:]
+            unique_codes[-1] = -1
+        return take_1d(self.categories.values, unique_codes)
 
     def equals(self, other):
         """

pandas/tests/test_categorical.py

@@ -769,11 +769,17 @@ def test_min_max(self):
         self.assertEqual(_max, 1)
 
     def test_unique(self):
-        cat = Categorical(["a","b","c","d"])
-        exp = np.asarray(["a","b","c","d"])
+        cat = Categorical(["a","b"])
+        exp = np.asarray(["a","b"])
         res = cat.unique()
         self.assert_numpy_array_equal(res, exp)
-        self.assertEqual(type(res), type(exp))
+        cat = Categorical(["a","b","a","a"], categories=["a","b","c"])
+        res = cat.unique()
+        self.assert_numpy_array_equal(res, exp)
+        cat = Categorical(["a","b","a", np.nan], categories=["a","b","c"])
+        res = cat.unique()
+        exp = np.asarray(["a","b", np.nan], dtype=object)
+        self.assert_numpy_array_equal(res, exp)
 
     def test_mode(self):
         s = Categorical([1,1,2,4,5,5,5], categories=[5,4,3,2,1], ordered=True)