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BUG: Fix all-NaT when ArrowEA.astype to categorical #62055

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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v3.0.0.rst
Original file line number Diff line number Diff line change
Expand Up @@ -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
^^^^^^^^^^^^
Expand Down
13 changes: 13 additions & 0 deletions pandas/core/arrays/categorical.py
Original file line number Diff line number Diff line change
Expand Up @@ -73,6 +73,7 @@
NDArrayBackedExtensionArray,
ravel_compat,
)
from pandas.core.arrays.arrow.array import ArrowExtensionArray
from pandas.core.base import (
ExtensionArray,
NoNewAttributesMixin,
Expand Down Expand Up @@ -483,6 +484,18 @@ 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()
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this can be expensive, particularly in dt64tz. have you figured out why the problem is happening? i.e. if you step through the code, where does the first wrong result show up


codes = _get_codes_for_values(values, dtype.categories)

if null_mask.any():
Expand Down
20 changes: 20 additions & 0 deletions pandas/tests/arrays/categorical/test_astype.py
Original file line number Diff line number Diff line change
Expand Up @@ -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:
Expand Down Expand Up @@ -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)))
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