beef up tests for +/- inf ranking , add tests for different dtypes

Author: peterpanmj

pandas/tests/series/test_rank.py

@@ -14,6 +14,8 @@
 from pandas.util.testing import assert_series_equal
 import pandas.util.testing as tm
 from pandas.tests.series.common import TestData
+from pandas._libs.tslib import iNaT
+from pandas._libs.algos import Infinity, NegInfinity
 
 
 class TestSeriesRank(TestData):
@@ -196,14 +198,40 @@ def test_rank_signature(self):
         pytest.raises(ValueError, s.rank, 'average')
 
     def test_rank_inf(self):
-        values = np.array(
+        # 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], dtype='float64')
-        random_order = np.random.permutation(len(values))
-        iseries = Series(values[random_order])
-        exp = Series(random_order + 1.0, dtype='float64')
-        iranks = iseries.rank()
-        assert_series_equal(iranks, exp)
+             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')
+        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)
 
     def test_rank_tie_methods(self):
         s = self.s