TST/CLN: deduplicate troublesome rank values (#38894)

* WIP

* TST/CLN: deduplicate troublesome rank values

* Remove unneeded imports

* Address comments

* Use frame_or_series instead

Author: mzeitlin11

pandas/tests/frame/methods/test_rank.py

@@ -392,7 +392,7 @@ def test_pct_max_many_rows(self):
             ([NegInfinity(), "1", "A", "BA", "Ba", "C", Infinity()], "object"),
         ],
     )
-    def test_rank_inf_and_nan(self, contents, dtype):
+    def test_rank_inf_and_nan(self, contents, dtype, frame_or_series):
         dtype_na_map = {
             "float64": np.nan,
             "float32": np.nan,
@@ -410,12 +410,13 @@ def test_rank_inf_and_nan(self, contents, dtype):
             nan_indices = np.random.choice(range(len(values)), 5)
             values = np.insert(values, nan_indices, na_value)
             exp_order = np.insert(exp_order, nan_indices, np.nan)
-        # shuffle the testing array and expected results in the same way
+
+        # Shuffle the testing array and expected results in the same way
         random_order = np.random.permutation(len(values))
-        df = DataFrame({"a": values[random_order]})
-        expected = DataFrame({"a": exp_order[random_order]}, dtype="float64")
-        result = df.rank()
-        tm.assert_frame_equal(result, expected)
+        obj = frame_or_series(values[random_order])
+        expected = frame_or_series(exp_order[random_order], dtype="float64")
+        result = obj.rank()
+        tm.assert_equal(result, expected)
 
     def test_df_series_inf_nan_consistency(self):
         # GH#32593

pandas/tests/series/methods/test_rank.py

@@ -3,7 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas._libs import iNaT
 from pandas._libs.algos import Infinity, NegInfinity
 import pandas.util._test_decorators as td
 
@@ -206,91 +205,6 @@ def test_rank_signature(self):
         with pytest.raises(ValueError, match=msg):
             s.rank("average")
 
-    @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 GH#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,
-        }
-        # Insert nans at random positions if underlying dtype has missing
-        # value. Then adjust the expected order by adding nans accordingly
-        # This is for testing whether rank calculation is affected
-        # when values are interwined with nan values.
-        values = np.array(contents, dtype=dtype)
-        exp_order = np.array(range(len(values)), dtype="float64") + 1.0
-        if dtype in dtype_na_map:
-            na_value = dtype_na_map[dtype]
-            nan_indices = np.random.choice(range(len(values)), 5)
-            values = np.insert(values, nan_indices, na_value)
-            exp_order = np.insert(exp_order, nan_indices, np.nan)
-        # shuffle the testing array and expected results in the same way
-        random_order = np.random.permutation(len(values))
-        iseries = Series(values[random_order])
-        exp = Series(exp_order[random_order], dtype="float64")
-        iranks = iseries.rank()
-        tm.assert_series_equal(iranks, exp)
-
     def test_rank_tie_methods(self):
         s = self.s