pandas-dev · sanggon6107 · Jan 23, 2025 · Jan 23, 2025 · Jan 23, 2025 · Jan 23, 2025
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
@@ -695,6 +695,7 @@ Indexing
 - Bug in :meth:`DataFrame.from_records` throwing a ``ValueError`` when passed an empty list in ``index`` (:issue:`58594`)
 - Bug in :meth:`DataFrame.loc` with inconsistent behavior of loc-set with 2 given indexes to Series (:issue:`59933`)
 - Bug in :meth:`MultiIndex.insert` when a new value inserted to a datetime-like level gets cast to ``NaT`` and fails indexing (:issue:`60388`)
+- Bug in :meth:`Series.mask` unexpectedly filling ``pd.NA`` (:issue:`60729`)
 - Bug in printing :attr:`Index.names` and :attr:`MultiIndex.levels` would not escape single quotes (:issue:`60190`)
 
 Missing

diff --git a/pandas/core/generic.py b/pandas/core/generic.py
@@ -9698,8 +9698,19 @@ def _where(
         if axis is not None:
             axis = self._get_axis_number(axis)
 
-        # align the cond to same shape as myself
         cond = common.apply_if_callable(cond, self)
+
+        # We should not be filling NA. See GH#60729
 # make sure we are boolean 
 fill_value = bool(inplace) 
 cond = cond.fillna(fill_value) 
 cond = cond.infer_objects() 
 # make sure we are boolean 
 fill_value = bool(inplace) 
 cond = cond.fillna(fill_value) 
 cond = cond.infer_objects() 
 if not isinstance(cond, ABCDataFrame): 
     # This is a single-dimensional object. 
     if not is_bool_dtype(cond): 
         raise TypeError(msg.format(dtype=cond.dtype)) 
 else: 
     for _dt in cond.dtypes: 
         if not is_bool_dtype(_dt): 
             raise TypeError(msg.format(dtype=_dt)) 
     if cond._mgr.any_extension_types: 
         # GH51574: avoid object ndarray conversion later on 
         cond = cond._constructor( 
             cond.to_numpy(dtype=bool, na_value=fill_value), 
             **cond._construct_axes_dict(), 
         ) 
 def where(self, other, cond) -> list[Block]: 
     arr = self.values.T 
     cond = extract_bool_array(cond) 
 def extract_bool_array(mask: ArrayLike) -> npt.NDArray[np.bool_]: 
     """ 
     If we have a SparseArray or BooleanArray, convert it to ndarray[bool]. 
     """ 
     if isinstance(mask, ExtensionArray): 
         # We could have BooleanArray, Sparse[bool], ... 
         #  Except for BooleanArray, this is equivalent to just 
         #  np.asarray(mask, dtype=bool) 
         mask = mask.to_numpy(dtype=bool, na_value=False) 
     mask = np.asarray(mask, dtype=bool) 
     return mask 
 # make sure we are boolean 
 fill_value = bool(inplace) 
 cond = cond.fillna(fill_value) 
 cond = cond.infer_objects() 
 # make sure we are boolean 
 fill_value = bool(inplace) 
 cond = cond.fillna(fill_value) 
 cond = cond.infer_objects() 
 if not isinstance(cond, ABCDataFrame): 
     # This is a single-dimensional object. 
     if not is_bool_dtype(cond): 
         raise TypeError(msg.format(dtype=cond.dtype)) 
 else: 
     for _dt in cond.dtypes: 
         if not is_bool_dtype(_dt): 
             raise TypeError(msg.format(dtype=_dt)) 
     if cond._mgr.any_extension_types: 
         # GH51574: avoid object ndarray conversion later on 
         cond = cond._constructor( 
             cond.to_numpy(dtype=bool, na_value=fill_value), 
             **cond._construct_axes_dict(), 
         ) 
 def where(self, other, cond) -> list[Block]: 
     arr = self.values.T 
  
     cond = extract_bool_array(cond) 
  
 def extract_bool_array(mask: ArrayLike) -> npt.NDArray[np.bool_]: 
     """ 
     If we have a SparseArray or BooleanArray, convert it to ndarray[bool]. 
     """ 
     if isinstance(mask, ExtensionArray): 
         # We could have BooleanArray, Sparse[bool], ... 
         #  Except for BooleanArray, this is equivalent to just 
         #  np.asarray(mask, dtype=bool) 
         mask = mask.to_numpy(dtype=bool, na_value=False) 
  
     mask = np.asarray(mask, dtype=bool) 
     return mask 
+        if isinstance(cond, np.ndarray):
+            cond = np.array(cond)
+            cond[isna(cond)] = True
+        elif isinstance(cond, NDFrame):
+            cond = cond.fillna(True)
+        elif isinstance(cond, (list, tuple)):
+            cond = np.array(cond)
+            cond[isna(cond)] = True
+
+        # align the cond to same shape as myself
         if isinstance(cond, NDFrame):
             # CoW: Make sure reference is not kept alive
             if cond.ndim == 1 and self.ndim == 2:

diff --git a/pandas/tests/series/indexing/test_mask.py b/pandas/tests/series/indexing/test_mask.py
@@ -1,7 +1,9 @@
 import numpy as np
 import pytest
 
-from pandas import Series
+from pandas import (
+    Series,
+)
 import pandas._testing as tm
 
 
@@ -67,3 +69,15 @@ def test_mask_inplace():
     rs = s.copy()
     rs.mask(cond, -s, inplace=True)
     tm.assert_series_equal(rs, s.mask(cond, -s))
+
+
+@pytest.mark.parametrize("dtype", ["Int64", "int64[pyarrow]"])
+def test_mask_na(dtype):
+    if dtype == "int64[pyarrow]":
+        pytest.importorskip("pyarrow")
+    # We should not be filling pd.NA. See GH#60729
+    series = Series([None, 1, 2, None, 3, 4, None], dtype=dtype)
+    result = series.mask(series <= 2, -99)
+    expected = Series([None, -99, -99, None, 3, 4, None], dtype=dtype)
+
+    tm.assert_series_equal(result, expected)