BUG: parse_time_string with np.str_ obj

doc/source/whatsnew/v1.5.0.rst

@@ -264,6 +264,7 @@ Indexing
 - Bug in :meth:`Series.__setitem__` when setting ``boolean`` dtype values containing ``NA`` incorrectly raising instead of casting to ``boolean`` dtype (:issue:`45462`)
 - Bug in :meth:`Series.__setitem__` where setting :attr:`NA` into a numeric-dtpye :class:`Series` would incorrectly upcast to object-dtype rather than treating the value as ``np.nan`` (:issue:`44199`)
 - Bug in getting a column from a DataFrame with an object-dtype row index with datetime-like values: the resulting Series now preserves the exact object-dtype Index from the parent DataFrame (:issue:`42950`)
+- Bug in indexing on a :class:`DatetimeIndex` with a ``np.str_`` key incorrectly raising (:issue:`45580`)
 -
 
 Missing

pandas/_libs/tslibs/parsing.pyx

@@ -291,7 +291,7 @@ def parse_datetime_string(
     return dt
 
 
-def parse_time_string(arg: str, freq=None, dayfirst=None, yearfirst=None):
+def parse_time_string(arg, freq=None, dayfirst=None, yearfirst=None):
     """
     Try hard to parse datetime string, leveraging dateutil plus some extra
     goodies like quarter recognition.
@@ -312,6 +312,16 @@ def parse_time_string(arg: str, freq=None, dayfirst=None, yearfirst=None):
     str
         Describing resolution of parsed string.
     """
+    if type(arg) is not str:
+        # GH#45580 np.str_ satisfies isinstance(obj, str) but if we annotate
+        #  arg as "str" this raises here
+        if not isinstance(arg, np.str_):
+            raise TypeError(
+                "Argument 'arg' has incorrect type "
+                f"(expected str, got {type(arg).__name__})"
+            )
+        arg = str(arg)
+
     if is_offset_object(freq):
         freq = freq.rule_code
 

pandas/tests/indexing/test_loc.py

@@ -209,6 +209,13 @@ def test_loc_getitem_single_boolean_arg(self, obj, key, exp):
 
 class TestLocBaseIndependent:
     # Tests for loc that do not depend on subclassing Base
+    def test_loc_npstr(self):
+        # GH#45580
+        df = DataFrame(index=date_range("2021", "2022"))
+        result = df.loc[np.array(["2021/6/1"])[0] :]
+        expected = df.iloc[151:]
+        tm.assert_frame_equal(result, expected)
+
     @pytest.mark.parametrize(
         "msg, key",
         [