TST: Refactor test for complex categorical, xfailing

Only want to xfail just the complex case, so converted to a parametrize.

Author: TomAugspurger

pandas/tests/test_algos.py

@@ -819,55 +819,57 @@ def test_duplicated_with_nas(self):
         expected = np.array(trues + trues)
         tm.assert_numpy_array_equal(result, expected)
 
-    def test_numeric_object_likes(self):
-        cases = [np.array([1, 2, 1, 5, 3,
-                           2, 4, 1, 5, 6]),
-                 np.array([1.1, 2.2, 1.1, np.nan, 3.3,
-                           2.2, 4.4, 1.1, np.nan, 6.6]),
-                 np.array([1 + 1j, 2 + 2j, 1 + 1j, 5 + 5j, 3 + 3j,
-                           2 + 2j, 4 + 4j, 1 + 1j, 5 + 5j, 6 + 6j]),
-                 np.array(['a', 'b', 'a', 'e', 'c',
-                           'b', 'd', 'a', 'e', 'f'], dtype=object),
-                 np.array([1, 2**63, 1, 3**5, 10,
-                           2**63, 39, 1, 3**5, 7], dtype=np.uint64)]
-
+    @pytest.mark.parametrize('case', [
+        np.array([1, 2, 1, 5, 3,
+                  2, 4, 1, 5, 6]),
+        np.array([1.1, 2.2, 1.1, np.nan, 3.3,
+                  2.2, 4.4, 1.1, np.nan, 6.6]),
+        pytest.mark.xfail(resaon="Complex bug. GH 16399")(
+            np.array([1 + 1j, 2 + 2j, 1 + 1j, 5 + 5j, 3 + 3j,
+                     2 + 2j, 4 + 4j, 1 + 1j, 5 + 5j, 6 + 6j])
+        ),
+        np.array(['a', 'b', 'a', 'e', 'c',
+                  'b', 'd', 'a', 'e', 'f'], dtype=object),
+        np.array([1, 2**63, 1, 3**5, 10, 2**63, 39, 1, 3**5, 7],
+                 dtype=np.uint64),
+    ])
+    def test_numeric_object_likes(self, case):
         exp_first = np.array([False, False, True, False, False,
                               True, False, True, True, False])
         exp_last = np.array([True, True, True, True, False,
                              False, False, False, False, False])
         exp_false = exp_first | exp_last
 
-        for case in cases:
-            res_first = algos.duplicated(case, keep='first')
-            tm.assert_numpy_array_equal(res_first, exp_first)
+        res_first = algos.duplicated(case, keep='first')
+        tm.assert_numpy_array_equal(res_first, exp_first)
 
-            res_last = algos.duplicated(case, keep='last')
-            tm.assert_numpy_array_equal(res_last, exp_last)
+        res_last = algos.duplicated(case, keep='last')
+        tm.assert_numpy_array_equal(res_last, exp_last)
 
-            res_false = algos.duplicated(case, keep=False)
-            tm.assert_numpy_array_equal(res_false, exp_false)
+        res_false = algos.duplicated(case, keep=False)
+        tm.assert_numpy_array_equal(res_false, exp_false)
 
-            # index
-            for idx in [pd.Index(case), pd.Index(case, dtype='category')]:
-                res_first = idx.duplicated(keep='first')
-                tm.assert_numpy_array_equal(res_first, exp_first)
+        # index
+        for idx in [pd.Index(case), pd.Index(case, dtype='category')]:
+            res_first = idx.duplicated(keep='first')
+            tm.assert_numpy_array_equal(res_first, exp_first)
 
-                res_last = idx.duplicated(keep='last')
-                tm.assert_numpy_array_equal(res_last, exp_last)
+            res_last = idx.duplicated(keep='last')
+            tm.assert_numpy_array_equal(res_last, exp_last)
 
-                res_false = idx.duplicated(keep=False)
-                tm.assert_numpy_array_equal(res_false, exp_false)
+            res_false = idx.duplicated(keep=False)
+            tm.assert_numpy_array_equal(res_false, exp_false)
 
-            # series
-            for s in [Series(case), Series(case, dtype='category')]:
-                res_first = s.duplicated(keep='first')
-                tm.assert_series_equal(res_first, Series(exp_first))
+        # series
+        for s in [Series(case), Series(case, dtype='category')]:
+            res_first = s.duplicated(keep='first')
+            tm.assert_series_equal(res_first, Series(exp_first))
 
-                res_last = s.duplicated(keep='last')
-                tm.assert_series_equal(res_last, Series(exp_last))
+            res_last = s.duplicated(keep='last')
+            tm.assert_series_equal(res_last, Series(exp_last))
 
-                res_false = s.duplicated(keep=False)
-                tm.assert_series_equal(res_false, Series(exp_false))
+            res_false = s.duplicated(keep=False)
+            tm.assert_series_equal(res_false, Series(exp_false))
 
     def test_datetime_likes(self):