Backport PR #55362 on branch 2.1.x (BUG: rank raising for arrow strin…

…g dtypes) (#55406)

doc/source/whatsnew/v2.1.2.rst

@@ -27,6 +27,7 @@ Bug fixes
 - Fixed bug in :meth:`DataFrame.interpolate` raising incorrect error message (:issue:`55347`)
 - Fixed bug in :meth:`Index.insert` raising when inserting ``None`` into :class:`Index` with ``dtype="string[pyarrow_numpy]"`` (:issue:`55365`)
 - Fixed bug in :meth:`Series.all`  and :meth:`Series.any` not treating missing values correctly for ``dtype="string[pyarrow_numpy]"`` (:issue:`55367`)
+- Fixed bug in :meth:`Series.rank` for ``string[pyarrow_numpy]`` dtype (:issue:`55362`)
 - Silence ``Period[B]`` warnings introduced by :issue:`53446` during normal plotting activity (:issue:`55138`)
 -
 

pandas/core/arrays/arrow/array.py

@@ -1712,7 +1712,7 @@ def __setitem__(self, key, value) -> None:
             data = pa.chunked_array([data])
         self._pa_array = data
 
-    def _rank(
+    def _rank_calc(
         self,
         *,
         axis: AxisInt = 0,
@@ -1721,9 +1721,6 @@ def _rank(
         ascending: bool = True,
         pct: bool = False,
     ):
-        """
-        See Series.rank.__doc__.
-        """
         if pa_version_under9p0 or axis != 0:
             ranked = super()._rank(
                 axis=axis,
@@ -1738,7 +1735,7 @@ def _rank(
             else:
                 pa_type = pa.uint64()
             result = pa.array(ranked, type=pa_type, from_pandas=True)
-            return type(self)(result)
+            return result
 
         data = self._pa_array.combine_chunks()
         sort_keys = "ascending" if ascending else "descending"
@@ -1777,7 +1774,29 @@ def _rank(
                 divisor = pc.count(result)
             result = pc.divide(result, divisor)
 
-        return type(self)(result)
+        return result
+
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        return type(self)(
+            self._rank_calc(
+                axis=axis,
+                method=method,
+                na_option=na_option,
+                ascending=ascending,
+                pct=pct,
+            )
+        )
 
     def _quantile(self, qs: npt.NDArray[np.float64], interpolation: str) -> Self:
         """

pandas/core/arrays/string_arrow.py

@@ -48,6 +48,7 @@
 
 if TYPE_CHECKING:
     from pandas._typing import (
+        AxisInt,
         Dtype,
         Scalar,
         npt,
@@ -444,6 +445,31 @@ def _str_rstrip(self, to_strip=None):
             result = pc.utf8_rtrim(self._pa_array, characters=to_strip)
         return type(self)(result)
 
+    def _convert_int_dtype(self, result):
+        return Int64Dtype().__from_arrow__(result)
+
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        return self._convert_int_dtype(
+            self._rank_calc(
+                axis=axis,
+                method=method,
+                na_option=na_option,
+                ascending=ascending,
+                pct=pct,
+            )
+        )
+
 
 class ArrowStringArrayNumpySemantics(ArrowStringArray):
     _storage = "pyarrow_numpy"
@@ -527,6 +553,10 @@ def _str_map(
             return lib.map_infer_mask(arr, f, mask.view("uint8"))
 
     def _convert_int_dtype(self, result):
+        if isinstance(result, pa.Array):
+            result = result.to_numpy(zero_copy_only=False)
+        elif not isinstance(result, np.ndarray):
+            result = result.to_numpy()
         if result.dtype == np.int32:
             result = result.astype(np.int64)
         return result

pandas/tests/frame/methods/test_rank.py

@@ -488,3 +488,15 @@ def test_rank_mixed_axis_zero(self, data, expected):
             df.rank()
         result = df.rank(numeric_only=True)
         tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype, exp_dtype",
+        [("string[pyarrow]", "Int64"), ("string[pyarrow_numpy]", "float64")],
+    )
+    def test_rank_string_dtype(self, dtype, exp_dtype):
+        # GH#55362
+        pytest.importorskip("pyarrow")
+        obj = Series(["foo", "foo", None, "foo"], dtype=dtype)
+        result = obj.rank(method="first")
+        expected = Series([1, 2, None, 3], dtype=exp_dtype)
+        tm.assert_series_equal(result, expected)