API: Change Series/DataFrame.other default to no_default instead of np.nan (pandas-dev#49156)

Author: mroeschke

doc/source/whatsnew/v2.0.0.rst

@@ -126,7 +126,7 @@ Other API changes
 - Passing data with dtype of "timedelta64[s]", "timedelta64[ms]", or "timedelta64[us]" to :class:`TimedeltaIndex`, :class:`Series`, or :class:`DataFrame` constructors will now retain that dtype instead of casting to "timedelta64[ns]"; timedelta64 data with lower resolution will be cast to the lowest supported resolution "timedelta64[s]" (:issue:`49014`)
 - Passing ``dtype`` of "timedelta64[s]", "timedelta64[ms]", or "timedelta64[us]" to :class:`TimedeltaIndex`, :class:`Series`, or :class:`DataFrame` constructors will now retain that dtype instead of casting to "timedelta64[ns]"; passing a dtype with lower resolution for :class:`Series` or :class:`DataFrame` will be cast to the lowest supported resolution "timedelta64[s]" (:issue:`49014`)
 - Passing a ``np.datetime64`` object with non-nanosecond resolution to :class:`Timestamp` will retain the input resolution if it is "s", "ms", or "ns"; otherwise it will be cast to the closest supported resolution (:issue:`49008`)
--
+- The ``other`` argument in :meth:`DataFrame.mask` and :meth:`Series.mask` now defaults to ``no_default`` instead of ``np.nan`` consistent with :meth:`DataFrame.where` and :meth:`Series.where`. Entries will be filled with the corresponding NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension dtypes). (:issue:`49111`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.deprecations:

pandas/core/frame.py

@@ -11998,7 +11998,7 @@ def mask(
     def mask(  # type: ignore[override]
         self,
         cond,
-        other=np.nan,
+        other=lib.no_default,
         inplace: bool = False,
         axis: Axis | None = None,
         level: Level = None,

pandas/core/generic.py

@@ -9871,6 +9871,9 @@ def where(
             If other is callable, it is computed on the {klass} and
             should return scalar or {klass}. The callable must not
             change input {klass} (though pandas doesn't check it).
+            If not specified, entries will be filled with the corresponding
+            NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension
+            dtypes).
         inplace : bool, default False
             Whether to perform the operation in place on the data.
         axis : int, default None
@@ -10072,7 +10075,7 @@ def mask(
     def mask(
         self: NDFrameT,
         cond,
-        other=np.nan,
+        other=lib.no_default,
         inplace: bool_t = False,
         axis: Axis | None = None,
         level: Level = None,

pandas/core/internals/blocks.py

@@ -1513,6 +1513,8 @@ def putmask(self, mask, new) -> list[Block]:
         See Block.putmask.__doc__
         """
         mask = extract_bool_array(mask)
+        if new is lib.no_default:
+            new = self.fill_value
 
         values = self.values
         if values.ndim == 2:

pandas/core/series.py

@@ -6205,7 +6205,7 @@ def mask(
     def mask(  # type: ignore[override]
         self,
         cond,
-        other=np.nan,
+        other=lib.no_default,
         inplace: bool = False,
         axis: Axis | None = None,
         level: Level = None,

pandas/tests/extension/test_arrow.py

@@ -856,17 +856,6 @@ def test_searchsorted(self, data_for_sorting, as_series, request):
             )
         super().test_searchsorted(data_for_sorting, as_series)
 
-    def test_where_series(self, data, na_value, as_frame, request):
-        pa_dtype = data.dtype.pyarrow_dtype
-        if pa.types.is_temporal(pa_dtype):
-            request.node.add_marker(
-                pytest.mark.xfail(
-                    raises=pa.ArrowNotImplementedError,
-                    reason=f"Unsupported cast from double to {pa_dtype}",
-                )
-            )
-        super().test_where_series(data, na_value, as_frame)
-
     def test_basic_equals(self, data):
         # https://github.com/pandas-dev/pandas/issues/34660
         assert pd.Series(data).equals(pd.Series(data))