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REGR: AttributeError: 'bool' object has no attribute 'to_numpy' in "mask_missing" #48313

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REGR: AttributeError: 'bool' object has no attribute 'to_numpy' in "mask_missing" #48313

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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.4.4.rst
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Expand Up @@ -33,6 +33,7 @@ Fixed regressions
- Fixed regression in :meth:`DatetimeIndex.intersection` when the :class:`DatetimeIndex` has dates crossing daylight savings time (:issue:`46702`)
- Fixed regression in :func:`merge` throwing an error when passing a :class:`Series` with a multi-level name (:issue:`47946`)
- Fixed regression in :meth:`DataFrame.eval` creating a copy when updating inplace (:issue:`47449`)
- Fixed regression where ``AttributeError`` may be raised when using ``pd.NA`` with ``object`` dtype (:issue:`47101`)
- Fixed regression where getting a row using :meth:`DataFrame.iloc` with :class:`SparseDtype` would raise (:issue:`46406`)

.. ---------------------------------------------------------------------------
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8 changes: 6 additions & 2 deletions pandas/core/missing.py
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Expand Up @@ -12,6 +12,7 @@
Any,
cast,
)
import warnings

import numpy as np

Expand Down Expand Up @@ -92,8 +93,11 @@ def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
# GH#29553 prevent numpy deprecation warnings
pass
else:
new_mask = arr == x
if not isinstance(new_mask, np.ndarray):
# GH#47101 we may get a scalar new_mask if elementwise comparison fails
with warnings.catch_warnings():
warnings.simplefilter("ignore", FutureWarning)
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is there a condition under which we can remove this check?

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I assume when the numpy behaviour changes and a FutureWarning is no longer given.

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Note: the warning was not being suppressed in 1.3.5 so not strictly needed for the regression fix.

new_mask = arr == x
if not isinstance(new_mask, (np.ndarray, bool)):
# usually BooleanArray
new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
mask |= new_mask
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7 changes: 7 additions & 0 deletions pandas/tests/frame/methods/test_replace.py
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Expand Up @@ -1496,6 +1496,13 @@ def test_replace_list_with_mixed_type(
result = obj.replace(box(to_replace), value)
tm.assert_equal(result, expected)

def test_replace_object_dtype_with_pandas_na(self):
# GH#47101
df = DataFrame({"d": [pd.NA]})
with tm.assert_produces_warning(None):
result = df.replace("", pd.NA)
tm.assert_frame_equal(result, df)


class TestDataFrameReplaceRegex:
@pytest.mark.parametrize(
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