REF: implement Manager.pad_or_backfill (pandas-dev#53822)

Author: jbrockmendel

pandas/core/generic.py

@@ -6912,7 +6912,7 @@ def _fillna_with_method(
 
             return result
 
-        new_mgr = self._mgr.interpolate(
+        new_mgr = self._mgr.pad_or_backfill(
             method=method,
             axis=axis,
             limit=limit,
@@ -7989,9 +7989,6 @@ def interpolate(
                     stacklevel=find_stack_level(),
                 )
 
-        if method not in fillna_methods:
-            axis = self._info_axis_number
-
         if isinstance(obj.index, MultiIndex) and method != "linear":
             raise ValueError(
                 "Only `method=linear` interpolation is supported on MultiIndexes."
@@ -8008,17 +8005,32 @@ def interpolate(
 
         index = missing.get_interp_index(method, obj.index)
 
-        new_data = obj._mgr.interpolate(
-            method=method,
-            axis=axis,
-            index=index,
-            limit=limit,
-            limit_direction=limit_direction,
-            limit_area=limit_area,
-            inplace=inplace,
-            downcast=downcast,
-            **kwargs,
-        )
+        if method.lower() in fillna_methods:
+            # TODO(3.0): remove this case
+            new_data = obj._mgr.pad_or_backfill(
+                method=method,
+                axis=axis,
+                index=index,
+                limit=limit,
+                limit_direction=limit_direction,
+                limit_area=limit_area,
+                inplace=inplace,
+                downcast=downcast,
+                **kwargs,
+            )
+        else:
+            axis = self._info_axis_number
+            new_data = obj._mgr.interpolate(
+                method=method,
+                axis=axis,
+                index=index,
+                limit=limit,
+                limit_direction=limit_direction,
+                limit_area=limit_area,
+                inplace=inplace,
+                downcast=downcast,
+                **kwargs,
+            )
 
         result = self._constructor_from_mgr(new_data, axes=new_data.axes)
         if should_transpose:

pandas/core/internals/array_manager.py

@@ -357,6 +357,9 @@ def diff(self, n: int) -> Self:
         assert self.ndim == 2  # caller ensures
         return self.apply(algos.diff, n=n)
 
+    def pad_or_backfill(self, **kwargs) -> Self:
+        return self.apply_with_block("pad_or_backfill", swap_axis=False, **kwargs)
+
     def interpolate(self, **kwargs) -> Self:
         return self.apply_with_block("interpolate", swap_axis=False, **kwargs)
 

pandas/core/internals/blocks.py

@@ -1343,6 +1343,35 @@ def fillna(
             ]
         )
 
+    def pad_or_backfill(
+        self,
+        *,
+        method: FillnaOptions = "pad",
+        axis: AxisInt = 0,
+        index: Index | None = None,
+        inplace: bool = False,
+        limit: int | None = None,
+        limit_direction: Literal["forward", "backward", "both"] = "forward",
+        limit_area: str | None = None,
+        fill_value: Any | None = None,
+        downcast: Literal["infer"] | None = None,
+        using_cow: bool = False,
+        **kwargs,
+    ) -> list[Block]:
+        return self.interpolate(
+            method=method,
+            axis=axis,
+            index=index,
+            inplace=inplace,
+            limit=limit,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            fill_value=fill_value,
+            downcast=downcast,
+            using_cow=using_cow,
+            **kwargs,
+        )
+
     def interpolate(
         self,
         *,

pandas/core/internals/managers.py

@@ -415,6 +415,14 @@ def diff(self, n: int) -> Self:
         # only reached with self.ndim == 2
         return self.apply("diff", n=n)
 
+    def pad_or_backfill(self, inplace: bool, **kwargs) -> Self:
+        return self.apply(
+            "pad_or_backfill",
+            inplace=inplace,
+            **kwargs,
+            using_cow=using_copy_on_write(),
+        )
+
     def interpolate(self, inplace: bool, **kwargs) -> Self:
         return self.apply(
             "interpolate", inplace=inplace, **kwargs, using_cow=using_copy_on_write()