BUG: Fix all-NaT when ArrowEA.astype to categorical

doc/source/whatsnew/v3.0.0.rst

@@ -693,7 +693,7 @@ Categorical
 - Bug in :meth:`Categorical.astype` where ``copy=False`` would still trigger a copy of the codes (:issue:`62000`)
 - Bug in :meth:`DataFrame.pivot` and :meth:`DataFrame.set_index` raising an ``ArrowNotImplementedError`` for columns with pyarrow dictionary dtype (:issue:`53051`)
 - Bug in :meth:`Series.convert_dtypes` with ``dtype_backend="pyarrow"`` where empty :class:`CategoricalDtype` :class:`Series` raised an error or got converted to ``null[pyarrow]`` (:issue:`59934`)
--
+- Bug in :meth:`array.astype` where casting a pyarrow-backed array to a temporal :class:`CategoricalDtype` (e.g. with datetime or timedelta categories) raised or incorrectly converted values to all ``NaT`` (:issue:`62051`)
 
 Datetimelike
 ^^^^^^^^^^^^

pandas/core/indexes/base.py

@@ -3674,6 +3674,14 @@ def get_indexer(
         orig_target = target
         target = self._maybe_cast_listlike_indexer(target)
 
+        from pandas.api.types import is_timedelta64_dtype
+
+        if target.dtype == "string[pyarrow]" and is_timedelta64_dtype(self.dtype):
+            from pandas.core.arrays.timedeltas import sequence_to_td64ns
+
+            data, freq = sequence_to_td64ns(target, copy=False, unit=None)
+            target = type(target)(data)
+
         self._check_indexing_method(method, limit, tolerance)
 
         if not self._index_as_unique:

pandas/core/indexes/datetimes.py

@@ -384,6 +384,18 @@ def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
         if self.tz is not None:
             # If we have tz, we can compare to tzaware
             return isinstance(dtype, DatetimeTZDtype)
+
+        from pandas import ArrowDtype
+
+        if isinstance(dtype, ArrowDtype):
+            import pyarrow as pa
+
+            return (
+                pa.types.is_date32(dtype.pyarrow_dtype)
+                or pa.types.is_date64(dtype.pyarrow_dtype)
+                or pa.types.is_timestamp(dtype.pyarrow_dtype)
+            )
+
         # if we dont have tz, we can only compare to tznaive
         return lib.is_np_dtype(dtype, "M")
 

pandas/tests/arrays/categorical/test_astype.py

@@ -7,14 +7,17 @@
     CategoricalDtype,
     CategoricalIndex,
     DatetimeIndex,
+    Index,
     Interval,
     NaT,
     Period,
     Timestamp,
     array,
+    isna,
     to_datetime,
 )
 import pandas._testing as tm
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
 
 
 class TestAstype:
@@ -160,3 +163,20 @@ def test_astype_category_readonly_mask_values(self):
         result = arr.astype("category")
         expected = array([0, 1, 2], dtype="Int64").astype("category")
         tm.assert_extension_array_equal(result, expected)
+
+    def test_arrow_array_astype_to_categorical_dtype_temporal(self):
+        # GH#62051
+        pytest.importorskip("pyarrow")
+        arr = array(
+            ["2017-01-01", "2018-01-01", "2019-01-01"], dtype="date32[day][pyarrow]"
+        )
+        cats = Index(["2017-01-01", "2018-01-01", "2019-01-01"], dtype="M8[s]")
+        dtype = CategoricalDtype(categories=cats, ordered=False)
+
+        assert not all(isna(arr.astype(dtype)))
+
+        arr = ArrowExtensionArray._from_sequence(["1h", "2h", "3h"])
+        cats = Index(["1h", "2h", "3h"], dtype="m8[ns]")
+        dtype = CategoricalDtype(cats, ordered=False)
+
+        assert not all(isna(arr.astype(dtype)))