paramterize tests on ranking +/- inf, xfail int64 dtype

Author: peterpanmj

pandas/tests/series/test_rank.py

@@ -197,41 +197,45 @@ def test_rank_signature(self):
         s.rank(method='average')
         pytest.raises(ValueError, s.rank, 'average')
 
-    def test_rank_inf(self):
-        # test ranking -/+ inf together with randomly inserted na values
-        mock_contents = (
-            [-np.inf, -50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10,
-             2, 40, np.inf],
-            [-np.inf, -50, -1, -1e-20, -1e-25, -1e-45, 0, 1e-40, 1e-20, 1e-10,
-             2, 40, np.inf],
-            [np.iinfo(np.uint8).min, 1, 2, 100, np.iinfo(np.uint8).max],
-            # -inf is eqivatlent to na value for int64 dtype becasue iNaT is
-            # the same as np.iinfo(np.int64).min. The expected rank for iNaT is
-            # nan if keep_na is true. Pending GH issue #16674
-            [np.iinfo(np.int64).min + 1, -100, 0, 1, 9999, 100000,
-             1e10, np.iinfo(np.int64).max],
-            [NegInfinity(), '1', 'A', 'BA', 'Ba', 'C', Infinity()]
-        )
-        dtypes = ('float64', 'float32', 'uint8', 'int64', 'object')
+    @pytest.mark.parametrize('contents,dtype', [
+        ([-np.inf, -50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10,
+          2, 40, np.inf],
+         'float64'),
+        ([-np.inf, -50, -1, -1e-20, -1e-25, -1e-45, 0, 1e-40, 1e-20, 1e-10,
+          2, 40, np.inf],
+         'float32'),
+        ([np.iinfo(np.uint8).min, 1, 2, 100, np.iinfo(np.uint8).max],
+         'uint8'),
+        pytest.param([np.iinfo(np.int64).min, -100, 0, 1, 9999, 100000,
+                      1e10, np.iinfo(np.int64).max],
+                     'int64',
+                     marks=pytest.mark.xfail(reason='''iNaT is equivalent to
+                                                      minimum value of dtype
+                                                      int64 pending issue
+                                                      #16674'''),
+                     ),
+        ([NegInfinity(), '1', 'A', 'BA', 'Ba', 'C', Infinity()],
+         'object')
+    ])
+    def test_rank_inf(self, contents, dtype):
         dtype_na_map = {
             'float64': np.nan,
             'float32': np.nan,
             'int64': iNaT,
             'object': None
         }
-        for content, dtype in zip(mock_contents, dtypes):
-            values = np.array(content, dtype=dtype)
-            nan_indices = np.random.choice(range(len(values)), 3)
-            exp_order = np.array(range(len(values)), dtype='float64') + 1.0
-            if dtype in dtype_na_map:
-                na_value = dtype_na_map[dtype]
-                values = np.insert(values, nan_indices, na_value)
-                exp_order = np.insert(exp_order, nan_indices, np.nan)
-            random_order = np.random.permutation(len(values))
-            iseries = Series(values[random_order])
-            exp = Series(exp_order[random_order], dtype='float64')
-            iranks = iseries.rank()
-            assert_series_equal(iranks, exp)
+        values = np.array(contents, dtype=dtype)
+        nan_indices = np.random.choice(range(len(values)), 3)
+        exp_order = np.array(range(len(values)), dtype='float64') + 1.0
+        if dtype in dtype_na_map:
+            na_value = dtype_na_map[dtype]
+            values = np.insert(values, nan_indices, na_value)
+            exp_order = np.insert(exp_order, nan_indices, np.nan)
+        random_order = np.random.permutation(len(values))
+        iseries = Series(values[random_order])
+        exp = Series(exp_order[random_order], dtype='float64')
+        iranks = iseries.rank()
+        assert_series_equal(iranks, exp)
 
     def test_rank_tie_methods(self):
         s = self.s