CLN: Enforce deprecations for EA.fillna

doc/source/whatsnew/v3.0.0.rst

@@ -34,7 +34,6 @@ Other enhancements
 - Allow dictionaries to be passed to :meth:`pandas.Series.str.replace` via ``pat`` parameter (:issue:`51748`)
 - Support passing a :class:`Series` input to :func:`json_normalize` that retains the :class:`Series` :class:`Index` (:issue:`51452`)
 - Users can globally disable any ``PerformanceWarning`` by setting the option ``mode.performance_warnings`` to ``False`` (:issue:`56920`)
--
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.notable_bug_fixes:
@@ -258,6 +257,7 @@ Removal of prior version deprecations/changes
 - Unrecognized timezones when parsing strings to datetimes now raises a ``ValueError`` (:issue:`51477`)
 - Removed the :class:`Grouper` attributes ``ax``, ``groups``, ``indexer``, and ``obj`` (:issue:`51206`, :issue:`51182`)
 - Removed deprecated keyword ``verbose`` on :func:`read_csv` and :func:`read_table` (:issue:`56556`)
+- Removed the ``method`` keyword in ``ExtensionArray.fillna``, implement ``ExtensionArray._pad_or_backfill`` instead (:issue:`53621`)
 - Removed the attribute ``dtypes`` from :class:`.DataFrameGroupBy` (:issue:`51997`)
 
 .. ---------------------------------------------------------------------------

pandas/core/arrays/_mixins.py

@@ -33,7 +33,6 @@
 from pandas.util._decorators import doc
 from pandas.util._validators import (
     validate_bool_kwarg,
-    validate_fillna_kwargs,
     validate_insert_loc,
 )
 
@@ -336,13 +335,7 @@ def _pad_or_backfill(
         return new_values
 
     @doc(ExtensionArray.fillna)
-    def fillna(
-        self, value=None, method=None, limit: int | None = None, copy: bool = True
-    ) -> Self:
-        value, method = validate_fillna_kwargs(
-            value, method, validate_scalar_dict_value=False
-        )
-
+    def fillna(self, value=None, limit: int | None = None, copy: bool = True) -> Self:
         mask = self.isna()
         # error: Argument 2 to "check_value_size" has incompatible type
         # "ExtensionArray"; expected "ndarray"
@@ -353,25 +346,12 @@ def fillna(
         )
 
         if mask.any():
-            if method is not None:
-                # (for now) when self.ndim == 2, we assume axis=0
-                func = missing.get_fill_func(method, ndim=self.ndim)
-                npvalues = self._ndarray.T
-                if copy:
-                    npvalues = npvalues.copy()
-                func(npvalues, limit=limit, mask=mask.T)
-                npvalues = npvalues.T
-
-                # TODO: NumpyExtensionArray didn't used to copy, need tests
-                #  for this
-                new_values = self._from_backing_data(npvalues)
+            # fill with value
+            if copy:
+                new_values = self.copy()
             else:
-                # fill with value
-                if copy:
-                    new_values = self.copy()
-                else:
-                    new_values = self[:]
-                new_values[mask] = value
+                new_values = self[:]
+            new_values[mask] = value
         else:
             # We validate the fill_value even if there is nothing to fill
             if value is not None:

pandas/core/arrays/arrow/array.py

@@ -29,7 +29,6 @@
     pa_version_under13p0,
 )
 from pandas.util._decorators import doc
-from pandas.util._validators import validate_fillna_kwargs
 
 from pandas.core.dtypes.cast import (
     can_hold_element,
@@ -1068,6 +1067,7 @@ def _pad_or_backfill(
                 #   a kernel for duration types.
                 pass
 
+        # TODO: Why do we no longer need the above cases?
         # TODO(3.0): after EA.fillna 'method' deprecation is enforced, we can remove
         #  this method entirely.
         return super()._pad_or_backfill(
@@ -1078,21 +1078,15 @@ def _pad_or_backfill(
     def fillna(
         self,
         value: object | ArrayLike | None = None,
-        method: FillnaOptions | None = None,
         limit: int | None = None,
         copy: bool = True,
     ) -> Self:
-        value, method = validate_fillna_kwargs(value, method)
-
         if not self._hasna:
             # TODO(CoW): Not necessary anymore when CoW is the default
             return self.copy()
 
         if limit is not None:
-            return super().fillna(value=value, method=method, limit=limit, copy=copy)
-
-        if method is not None:
-            return super().fillna(method=method, limit=limit, copy=copy)
+            return super().fillna(value=value, limit=limit, copy=copy)
 
         if isinstance(value, (np.ndarray, ExtensionArray)):
             # Similar to check_value_size, but we do not mask here since we may
@@ -1118,7 +1112,7 @@ def fillna(
             #   a kernel for duration types.
             pass
 
-        return super().fillna(value=value, method=method, limit=limit, copy=copy)
+        return super().fillna(value=value, limit=limit, copy=copy)
 
     def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         # short-circuit to return all False array.

pandas/core/arrays/base.py

@@ -38,7 +38,6 @@
 from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import (
     validate_bool_kwarg,
-    validate_fillna_kwargs,
     validate_insert_loc,
 )
 
@@ -1007,31 +1006,6 @@ def _pad_or_backfill(
         [<NA>, 2, 2, 3, <NA>, <NA>]
         Length: 6, dtype: Int64
         """
-
-        # If a 3rd-party EA has implemented this functionality in fillna,
-        #  we warn that they need to implement _pad_or_backfill instead.
-        if (
-            type(self).fillna is not ExtensionArray.fillna
-            and type(self)._pad_or_backfill is ExtensionArray._pad_or_backfill
-        ):
-            # Check for _pad_or_backfill here allows us to call
-            #  super()._pad_or_backfill without getting this warning
-            warnings.warn(
-                "ExtensionArray.fillna 'method' keyword is deprecated. "
-                "In a future version. arr._pad_or_backfill will be called "
-                "instead. 3rd-party ExtensionArray authors need to implement "
-                "_pad_or_backfill.",
-                DeprecationWarning,
-                stacklevel=find_stack_level(),
-            )
-            if limit_area is not None:
-                raise NotImplementedError(
-                    f"{type(self).__name__} does not implement limit_area "
-                    "(added in pandas 2.2). 3rd-party ExtnsionArray authors "
-                    "need to add this argument to _pad_or_backfill."
-                )
-            return self.fillna(method=method, limit=limit)
-
         mask = self.isna()
 
         if mask.any():
@@ -1057,8 +1031,7 @@ def _pad_or_backfill(
 
     def fillna(
         self,
-        value: object | ArrayLike | None = None,
-        method: FillnaOptions | None = None,
+        value: object | ArrayLike,
         limit: int | None = None,
         copy: bool = True,
     ) -> Self:
@@ -1071,24 +1044,13 @@ def fillna(
             If a scalar value is passed it is used to fill all missing values.
             Alternatively, an array-like "value" can be given. It's expected
             that the array-like have the same length as 'self'.
-        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
-            Method to use for filling holes in reindexed Series:
-
-            * pad / ffill: propagate last valid observation forward to next valid.
-            * backfill / bfill: use NEXT valid observation to fill gap.
-
-            .. deprecated:: 2.1.0
-
         limit : int, default None
             If method is specified, this is the maximum number of consecutive
             NaN values to forward/backward fill. In other words, if there is
             a gap with more than this number of consecutive NaNs, it will only
             be partially filled. If method is not specified, this is the
             maximum number of entries along the entire axis where NaNs will be
             filled.
-
-            .. deprecated:: 2.1.0
-
         copy : bool, default True
             Whether to make a copy of the data before filling. If False, then
             the original should be modified and no new memory should be allocated.
@@ -1110,16 +1072,6 @@ def fillna(
         [0, 0, 2, 3, 0, 0]
         Length: 6, dtype: Int64
         """
-        if method is not None:
-            warnings.warn(
-                f"The 'method' keyword in {type(self).__name__}.fillna is "
-                "deprecated and will be removed in a future version.",
-                FutureWarning,
-                stacklevel=find_stack_level(),
-            )
-
-        value, method = validate_fillna_kwargs(value, method)
-
         mask = self.isna()
         # error: Argument 2 to "check_value_size" has incompatible type
         # "ExtensionArray"; expected "ndarray"
@@ -1130,24 +1082,12 @@ def fillna(
         )
 
         if mask.any():
-            if method is not None:
-                meth = missing.clean_fill_method(method)
-
-                npmask = np.asarray(mask)
-                if meth == "pad":
-                    indexer = libalgos.get_fill_indexer(npmask, limit=limit)
-                    return self.take(indexer, allow_fill=True)
-                else:
-                    # i.e. meth == "backfill"
-                    indexer = libalgos.get_fill_indexer(npmask[::-1], limit=limit)[::-1]
-                    return self[::-1].take(indexer, allow_fill=True)
+            # fill with value
+            if not copy:
+                new_values = self[:]
             else:
-                # fill with value
-                if not copy:
-                    new_values = self[:]
-                else:
-                    new_values = self.copy()
-                new_values[mask] = value
+                new_values = self.copy()
+            new_values[mask] = value
         else:
             if not copy:
                 new_values = self[:]

pandas/core/arrays/interval.py

@@ -29,7 +29,6 @@
     ArrayLike,
     AxisInt,
     Dtype,
-    FillnaOptions,
     IntervalClosedType,
     NpDtype,
     PositionalIndexer,
@@ -894,23 +893,7 @@ def max(self, *, axis: AxisInt | None = None, skipna: bool = True) -> IntervalOr
         indexer = obj.argsort()[-1]
         return obj[indexer]
 
-    def _pad_or_backfill(  # pylint: disable=useless-parent-delegation
-        self,
-        *,
-        method: FillnaOptions,
-        limit: int | None = None,
-        limit_area: Literal["inside", "outside"] | None = None,
-        copy: bool = True,
-    ) -> Self:
-        # TODO(3.0): after EA.fillna 'method' deprecation is enforced, we can remove
-        #  this method entirely.
-        return super()._pad_or_backfill(
-            method=method, limit=limit, limit_area=limit_area, copy=copy
-        )
-
-    def fillna(
-        self, value=None, method=None, limit: int | None = None, copy: bool = True
-    ) -> Self:
+    def fillna(self, value=None, limit: int | None = None, copy: bool = True) -> Self:
         """
         Fill NA/NaN values using the specified method.
 
@@ -921,9 +904,6 @@ def fillna(
             Alternatively, a Series or dict can be used to fill in different
             values for each index. The value should not be a list. The
             value(s) passed should be either Interval objects or NA/NaN.
-        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
-            (Not implemented yet for IntervalArray)
-            Method to use for filling holes in reindexed Series
         limit : int, default None
             (Not implemented yet for IntervalArray)
             If method is specified, this is the maximum number of consecutive
@@ -944,8 +924,6 @@ def fillna(
         """
         if copy is False:
             raise NotImplementedError
-        if method is not None:
-            return super().fillna(value=value, method=method, limit=limit)
 
         value_left, value_right = self._validate_scalar(value)
 

pandas/core/arrays/masked.py

@@ -38,7 +38,6 @@
 )
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import doc
-from pandas.util._validators import validate_fillna_kwargs
 
 from pandas.core.dtypes.base import ExtensionDtype
 from pandas.core.dtypes.common import (
@@ -237,32 +236,18 @@ def _pad_or_backfill(
         return new_values
 
     @doc(ExtensionArray.fillna)
-    def fillna(
-        self, value=None, method=None, limit: int | None = None, copy: bool = True
-    ) -> Self:
-        value, method = validate_fillna_kwargs(value, method)
-
+    def fillna(self, value=None, limit: int | None = None, copy: bool = True) -> Self:
         mask = self._mask
 
         value = missing.check_value_size(value, mask, len(self))
 
         if mask.any():
-            if method is not None:
-                func = missing.get_fill_func(method, ndim=self.ndim)
-                npvalues = self._data.T
-                new_mask = mask.T
-                if copy:
-                    npvalues = npvalues.copy()
-                    new_mask = new_mask.copy()
-                func(npvalues, limit=limit, mask=new_mask)
-                return self._simple_new(npvalues.T, new_mask.T)
+            # fill with value
+            if copy:
+                new_values = self.copy()
             else:
-                # fill with value
-                if copy:
-                    new_values = self.copy()
-                else:
-                    new_values = self[:]
-                new_values[mask] = value
+                new_values = self[:]
+            new_values[mask] = value
         else:
             if copy:
                 new_values = self.copy()

pandas/core/arrays/period.py

@@ -847,19 +847,6 @@ def _pad_or_backfill(
         else:
             return self
 
-    def fillna(
-        self, value=None, method=None, limit: int | None = None, copy: bool = True
-    ) -> Self:
-        if method is not None:
-            # view as dt64 so we get treated as timelike in core.missing,
-            #  similar to dtl._period_dispatch
-            dta = self.view("M8[ns]")
-            result = dta.fillna(value=value, method=method, limit=limit, copy=copy)
-            # error: Incompatible return value type (got "Union[ExtensionArray,
-            # ndarray[Any, Any]]", expected "PeriodArray")
-            return result.view(self.dtype)  # type: ignore[return-value]
-        return super().fillna(value=value, method=method, limit=limit, copy=copy)
-
     # ------------------------------------------------------------------
     # Arithmetic Methods