REF: dispatch Series.rank to EA

pandas/core/algorithms.py

@@ -286,8 +286,6 @@ def _get_hashtable_algo(values: np.ndarray):
 
 
 def _get_values_for_rank(values: ArrayLike) -> np.ndarray:
-    if is_categorical_dtype(values):
-        values = cast("Categorical", values)._values_for_rank()
 
     values = _ensure_data(values)
     if values.dtype.kind in ["i", "u", "f"]:
@@ -993,13 +991,13 @@ def rank(
     na_option: str = "keep",
     ascending: bool = True,
     pct: bool = False,
-) -> np.ndarray:
+) -> npt.NDArray[np.float64]:
     """
     Rank the values along a given axis.
 
     Parameters
     ----------
-    values : array-like
+    values : np.ndarray or ExtensionArray
         Array whose values will be ranked. The number of dimensions in this
         array must not exceed 2.
     axis : int, default 0

pandas/core/arrays/base.py

@@ -73,6 +73,7 @@
 from pandas.core.algorithms import (
     factorize_array,
     isin,
+    rank,
     unique,
 )
 from pandas.core.array_algos.quantile import quantile_with_mask
@@ -1496,6 +1497,32 @@ def _fill_mask_inplace(
         self[mask] = new_values[mask]
         return
 
+    def _rank(
+        self,
+        *,
+        axis: int = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        if axis != 0:
+            raise NotImplementedError
+
+        # TODO: we only have tests that get here with dt64 and td64
+        # TODO: all tests that get here use the defaults for all the kwds
+        return rank(
+            self,
+            axis=axis,
+            method=method,
+            na_option=na_option,
+            ascending=ascending,
+            pct=pct,
+        )
+
     @classmethod
     def _empty(cls, shape: Shape, dtype: ExtensionDtype):
         """

pandas/core/arrays/categorical.py

@@ -1842,6 +1842,30 @@ def sort_values(
             codes = self._codes[sorted_idx]
             return self._from_backing_data(codes)
 
+    def _rank(
+        self,
+        *,
+        axis: int = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        if axis != 0:
+            raise NotImplementedError
+        vff = self._values_for_rank()
+        return algorithms.rank(
+            vff,
+            axis=axis,
+            method=method,
+            na_option=na_option,
+            ascending=ascending,
+            pct=pct,
+        )
+
     def _values_for_rank(self):
         """
         For correctly ranking ordered categorical data. See GH#15420

pandas/core/generic.py

@@ -8494,19 +8494,32 @@ def rank(
             raise ValueError(msg)
 
         def ranker(data):
-            ranks = algos.rank(
-                data.values,
-                axis=axis,
-                method=method,
-                ascending=ascending,
-                na_option=na_option,
-                pct=pct,
-            )
-            # error: Argument 1 to "NDFrame" has incompatible type "ndarray"; expected
-            # "Union[ArrayManager, BlockManager]"
-            ranks_obj = self._constructor(
-                ranks, **data._construct_axes_dict()  # type: ignore[arg-type]
-            )
+            if data.ndim == 2:
+                # i.e. DataFrame, we cast to ndarray
+                values = data.values
+            else:
+                # i.e. Series, can dispatch to EA
+                values = data._values
+
+            if isinstance(values, ExtensionArray):
+                ranks = values._rank(
+                    axis=axis,
+                    method=method,
+                    ascending=ascending,
+                    na_option=na_option,
+                    pct=pct,
+                )
+            else:
+                ranks = algos.rank(
+                    values,
+                    axis=axis,
+                    method=method,
+                    ascending=ascending,
+                    na_option=na_option,
+                    pct=pct,
+                )
+
+            ranks_obj = self._constructor(ranks, **data._construct_axes_dict())
             return ranks_obj.__finalize__(self, method="rank")
 
         # if numeric_only is None, and we can't get anything, we try with