diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index f73ef36f76086..a07a2112faba1 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -143,6 +143,7 @@
     arraylike,
     common,
     indexing,
+    missing,
     nanops,
     sample,
 )
@@ -7907,20 +7908,7 @@ def interpolate(
                 "Only `method=linear` interpolation is supported on MultiIndexes."
             )
 
-        # Set `limit_direction` depending on `method`
-        if limit_direction is None:
-            limit_direction = (
-                "backward" if method in ("backfill", "bfill") else "forward"
-            )
-        else:
-            if method in ("pad", "ffill") and limit_direction != "forward":
-                raise ValueError(
-                    f"`limit_direction` must be 'forward' for method `{method}`"
-                )
-            if method in ("backfill", "bfill") and limit_direction != "backward":
-                raise ValueError(
-                    f"`limit_direction` must be 'backward' for method `{method}`"
-                )
+        limit_direction = missing.infer_limit_direction(limit_direction, method)
 
         if obj.ndim == 2 and np.all(obj.dtypes == np.dtype("object")):
             raise TypeError(
@@ -7929,32 +7917,8 @@ def interpolate(
                 "column to a numeric dtype."
             )
 
-        # create/use the index
-        if method == "linear":
-            # prior default
-            index = Index(np.arange(len(obj.index)))
-        else:
-            index = obj.index
-            methods = {"index", "values", "nearest", "time"}
-            is_numeric_or_datetime = (
-                is_numeric_dtype(index.dtype)
-                or isinstance(index.dtype, DatetimeTZDtype)
-                or lib.is_np_dtype(index.dtype, "mM")
-            )
-            if method not in methods and not is_numeric_or_datetime:
-                raise ValueError(
-                    "Index column must be numeric or datetime type when "
-                    f"using {method} method other than linear. "
-                    "Try setting a numeric or datetime index column before "
-                    "interpolating."
-                )
+        index = missing.get_interp_index(method, obj.index)
 
-        if isna(index).any():
-            raise NotImplementedError(
-                "Interpolation with NaNs in the index "
-                "has not been implemented. Try filling "
-                "those NaNs before interpolating."
-            )
         new_data = obj._mgr.interpolate(
             method=method,
             axis=axis,
@@ -8140,13 +8104,13 @@ def asof(self, where, subset=None):
         locs = self.index.asof_locs(where, ~(nulls._values))
 
         # mask the missing
-        missing = locs == -1
+        mask = locs == -1
         data = self.take(locs)
         data.index = where
-        if missing.any():
+        if mask.any():
             # GH#16063 only do this setting when necessary, otherwise
             #  we'd cast e.g. bools to floats
-            data.loc[missing] = np.nan
+            data.loc[mask] = np.nan
         return data if is_list else data.iloc[-1]
 
     # ----------------------------------------------------------------------
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 7c5d686d96939..4244b7bc0c881 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -1350,7 +1350,7 @@ def interpolate(
         index: Index | None = None,
         inplace: bool = False,
         limit: int | None = None,
-        limit_direction: str = "forward",
+        limit_direction: Literal["forward", "backward", "both"] = "forward",
         limit_area: str | None = None,
         fill_value: Any | None = None,
         downcast: Literal["infer"] | None = None,
diff --git a/pandas/core/missing.py b/pandas/core/missing.py
index 7762ba8e2c730..2fe6be9ff48c1 100644
--- a/pandas/core/missing.py
+++ b/pandas/core/missing.py
@@ -33,10 +33,12 @@
 from pandas.core.dtypes.cast import infer_dtype_from
 from pandas.core.dtypes.common import (
     is_array_like,
+    is_numeric_dtype,
     is_numeric_v_string_like,
     is_object_dtype,
     needs_i8_conversion,
 )
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
 from pandas.core.dtypes.missing import (
     is_valid_na_for_dtype,
     isna,
@@ -225,6 +227,56 @@ def find_valid_index(how: str, is_valid: npt.NDArray[np.bool_]) -> int | None:
     return idxpos  # type: ignore[return-value]
 
 
+def infer_limit_direction(limit_direction, method):
+    # Set `limit_direction` depending on `method`
+    if limit_direction is None:
+        if method in ("backfill", "bfill"):
+            limit_direction = "backward"
+        else:
+            limit_direction = "forward"
+    else:
+        if method in ("pad", "ffill") and limit_direction != "forward":
+            raise ValueError(
+                f"`limit_direction` must be 'forward' for method `{method}`"
+            )
+        if method in ("backfill", "bfill") and limit_direction != "backward":
+            raise ValueError(
+                f"`limit_direction` must be 'backward' for method `{method}`"
+            )
+    return limit_direction
+
+
+def get_interp_index(method, index: Index) -> Index:
+    # create/use the index
+    if method == "linear":
+        # prior default
+        from pandas import Index
+
+        index = Index(np.arange(len(index)))
+    else:
+        methods = {"index", "values", "nearest", "time"}
+        is_numeric_or_datetime = (
+            is_numeric_dtype(index.dtype)
+            or isinstance(index.dtype, DatetimeTZDtype)
+            or lib.is_np_dtype(index.dtype, "mM")
+        )
+        if method not in methods and not is_numeric_or_datetime:
+            raise ValueError(
+                "Index column must be numeric or datetime type when "
+                f"using {method} method other than linear. "
+                "Try setting a numeric or datetime index column before "
+                "interpolating."
+            )
+
+    if isna(index).any():
+        raise NotImplementedError(
+            "Interpolation with NaNs in the index "
+            "has not been implemented. Try filling "
+            "those NaNs before interpolating."
+        )
+    return index
+
+
 def interpolate_array_2d(
     data: np.ndarray,
     method: str = "pad",