Update fix logic

Author: arthurlw

pandas/core/arrays/categorical.py

@@ -73,7 +73,6 @@
     NDArrayBackedExtensionArray,
     ravel_compat,
 )
-from pandas.core.arrays.arrow.array import ArrowExtensionArray
 from pandas.core.base import (
     ExtensionArray,
     NoNewAttributesMixin,
@@ -484,18 +483,6 @@ def __init__(
             )
 
         else:
-            if isinstance(values, ArrowExtensionArray):
-                from pandas.api.types import (
-                    is_datetime64_any_dtype,
-                    is_timedelta64_dtype,
-                )
-
-                cat_dtype = dtype.categories.dtype
-                if is_datetime64_any_dtype(cat_dtype) or is_timedelta64_dtype(
-                    cat_dtype
-                ):
-                    values = values.to_numpy()
-
             codes = _get_codes_for_values(values, dtype.categories)
 
         if null_mask.any():

pandas/core/indexes/base.py

@@ -3674,6 +3674,16 @@ def get_indexer(
         orig_target = target
         target = self._maybe_cast_listlike_indexer(target)
 
+        from pandas.api.types import is_timedelta64_dtype
+        if (
+            self.dtype == "string[pyarrow]" and is_timedelta64_dtype(target.dtype)
+        ) or (
+            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:
@@ -6273,6 +6283,16 @@ def _find_common_type_compat(self, target) -> DtypeObj:
             ):
                 return _dtype_obj
 
+        # from pandas.api.types import is_timedelta64_dtype
+        # from pandas.core.arrays.timedeltas import sequence_to_td64ns
+
+        # if (
+        #     self.dtype == "string[pyarrow]" and is_timedelta64_dtype(target_dtype)
+        # ) or (
+        #     target_dtype == "string[pyarrow]" and is_timedelta64_dtype(self.dtype)
+        # ):
+        #     return np.dtype("m8[ns]")
+
         dtype = find_result_type(self.dtype, target)
         dtype = common_dtype_categorical_compat([self, target], dtype)
         return dtype

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")