TST: fix xfailed algos tests

Author: jbrockmendel

pandas/tests/test_algos.py

@@ -4,8 +4,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 from pandas._libs import (
     algos as libalgos,
     hashtable as ht,
@@ -1684,20 +1682,25 @@ def test_unique_complex_numbers(self, array, expected):
 
 
 class TestHashTable:
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
     @pytest.mark.parametrize(
         "htable, data",
         [
-            (ht.PyObjectHashTable, [f"foo_{i}" for i in range(1000)]),
-            (ht.StringHashTable, [f"foo_{i}" for i in range(1000)]),
+            (
+                ht.PyObjectHashTable,
+                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
+            ),
+            (
+                ht.StringHashTable,
+                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
+            ),
             (ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
             (ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
             (ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
         ],
     )
     def test_hashtable_unique(self, htable, data, writable):
         # output of maker has guaranteed unique elements
-        s = Series(data)
+        s = Series(data, dtype=data.dtype)
         if htable == ht.Float64HashTable:
             # add NaN for float column
             s.loc[500] = np.nan
@@ -1724,20 +1727,25 @@ def test_hashtable_unique(self, htable, data, writable):
         reconstr = result_unique[result_inverse]
         tm.assert_numpy_array_equal(reconstr, s_duplicated.values)
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
     @pytest.mark.parametrize(
         "htable, data",
         [
-            (ht.PyObjectHashTable, [f"foo_{i}" for i in range(1000)]),
-            (ht.StringHashTable, [f"foo_{i}" for i in range(1000)]),
+            (
+                ht.PyObjectHashTable,
+                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
+            ),
+            (
+                ht.StringHashTable,
+                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
+            ),
             (ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
             (ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
             (ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
         ],
     )
     def test_hashtable_factorize(self, htable, writable, data):
         # output of maker has guaranteed unique elements
-        s = Series(data)
+        s = Series(data, dtype=data.dtype)
         if htable == ht.Float64HashTable:
             # add NaN for float column
             s.loc[500] = np.nan