Skip to content

Backport PR #57388 on branch 2.2.x (BUG: map(na_action=ignore) not respected for Arrow & masked types) #57413

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.2.1.rst
Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@ Fixed regressions
- Fixed regression in :meth:`CategoricalIndex.difference` raising ``KeyError`` when other contains null values other than NaN (:issue:`57318`)
- Fixed regression in :meth:`DataFrame.groupby` raising ``ValueError`` when grouping by a :class:`Series` in some cases (:issue:`57276`)
- Fixed regression in :meth:`DataFrame.loc` raising ``IndexError`` for non-unique, masked dtype indexes where result has more than 10,000 rows (:issue:`57027`)
- Fixed regression in :meth:`DataFrame.map` with ``na_action="ignore"`` not being respected for NumPy nullable and :class:`ArrowDtypes` (:issue:`57316`)
- Fixed regression in :meth:`DataFrame.merge` raising ``ValueError`` for certain types of 3rd-party extension arrays (:issue:`57316`)
- Fixed regression in :meth:`DataFrame.shift` raising ``AssertionError`` for ``axis=1`` and empty :class:`DataFrame` (:issue:`57301`)
- Fixed regression in :meth:`DataFrame.sort_index` not producing a stable sort for a index with duplicates (:issue:`57151`)
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/arrays/arrow/array.py
Original file line number Diff line number Diff line change
Expand Up @@ -1414,7 +1414,7 @@ def to_numpy(

def map(self, mapper, na_action=None):
if is_numeric_dtype(self.dtype):
return map_array(self.to_numpy(), mapper, na_action=None)
return map_array(self.to_numpy(), mapper, na_action=na_action)
else:
return super().map(mapper, na_action)

Expand Down
2 changes: 1 addition & 1 deletion pandas/core/arrays/masked.py
Original file line number Diff line number Diff line change
Expand Up @@ -1333,7 +1333,7 @@ def max(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
return self._wrap_reduction_result("max", result, skipna=skipna, axis=axis)

def map(self, mapper, na_action=None):
return map_array(self.to_numpy(), mapper, na_action=None)
return map_array(self.to_numpy(), mapper, na_action=na_action)

def any(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
"""
Expand Down
7 changes: 7 additions & 0 deletions pandas/tests/extension/test_arrow.py
Original file line number Diff line number Diff line change
Expand Up @@ -3379,3 +3379,10 @@ def test_to_numpy_timestamp_to_int():
result = ser.to_numpy(dtype=np.int64)
expected = np.array([1577853000000000000])
tm.assert_numpy_array_equal(result, expected)


def test_map_numeric_na_action():
ser = pd.Series([32, 40, None], dtype="int64[pyarrow]")
result = ser.map(lambda x: 42, na_action="ignore")
expected = pd.Series([42.0, 42.0, np.nan], dtype="float64")
tm.assert_series_equal(result, expected)
9 changes: 9 additions & 0 deletions pandas/tests/extension/test_masked.py
Original file line number Diff line number Diff line change
Expand Up @@ -179,6 +179,15 @@ def test_map(self, data_missing, na_action):
expected = data_missing.to_numpy()
tm.assert_numpy_array_equal(result, expected)

def test_map_na_action_ignore(self, data_missing_for_sorting):
zero = data_missing_for_sorting[2]
result = data_missing_for_sorting.map(lambda x: zero, na_action="ignore")
if data_missing_for_sorting.dtype.kind == "b":
expected = np.array([False, pd.NA, False], dtype=object)
else:
expected = np.array([zero, np.nan, zero])
tm.assert_numpy_array_equal(result, expected)

def _get_expected_exception(self, op_name, obj, other):
try:
dtype = tm.get_dtype(obj)
Expand Down