PERF: Series(pyarrow-backed).rank

doc/source/whatsnew/v2.0.0.rst

@@ -734,6 +734,7 @@ Performance improvements
 - Performance improvement in :meth:`MultiIndex.isin` when ``level=None`` (:issue:`48622`, :issue:`49577`)
 - Performance improvement in :meth:`MultiIndex.putmask` (:issue:`49830`)
 - Performance improvement in :meth:`Index.union` and :meth:`MultiIndex.union` when index contains duplicates (:issue:`48900`)
+- Performance improvement in :meth:`Series.rank` for pyarrow-backed dtypes (:issue:`50264`)
 - Performance improvement in :meth:`Series.fillna` for extension array dtypes (:issue:`49722`, :issue:`50078`)
 - Performance improvement for :meth:`Series.value_counts` with nullable dtype (:issue:`48338`)
 - Performance improvement for :class:`Series` constructor passing integer numpy array with nullable dtype (:issue:`48338`)

pandas/core/arrays/arrow/array.py

@@ -11,6 +11,7 @@
 
 from pandas._typing import (
     ArrayLike,
+    AxisInt,
     Dtype,
     FillnaOptions,
     Iterator,
@@ -22,6 +23,7 @@
 from pandas.compat import (
     pa_version_under6p0,
     pa_version_under7p0,
+    pa_version_under9p0,
 )
 from pandas.util._decorators import doc
 from pandas.util._validators import validate_fillna_kwargs
@@ -949,7 +951,75 @@ def _indexing_key_to_indices(
             indices = np.arange(n)[key]
         return indices
 
-    # TODO: redefine _rank using pc.rank with pyarrow 9.0
+    def _rank(
+        self,
+        *,
+        axis: AxisInt = 0,
+        method: str = "average",
+        na_option: str = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ):
+        """
+        See Series.rank.__doc__.
+        """
+        if pa_version_under9p0:
+            ranked = super()._rank(
+                axis=axis,
+                method=method,
+                na_option=na_option,
+                ascending=ascending,
+                pct=pct,
+            )
+            # keep dtypes consistent with the implementation below
+            if method == "average" or pct:
+                pa_type = pa.float64()
+            else:
+                pa_type = pa.uint64()
+            result = pa.array(ranked, type=pa_type, from_pandas=True)
+            return type(self)(result)
+
+        if axis != 0:
+            raise NotImplementedError
+
+        data = self._data.combine_chunks()
+        sort_keys = "ascending" if ascending else "descending"
+        null_placement = "at_start" if na_option == "top" else "at_end"
+        tiebreaker = "min" if method == "average" else method
+
+        result = pc.rank(
+            data,
+            sort_keys=sort_keys,
+            null_placement=null_placement,
+            tiebreaker=tiebreaker,
+        )
+
+        if na_option == "keep":
+            mask = pc.is_null(self._data)
+            null = pa.scalar(None, type=result.type)
+            result = pc.if_else(mask, null, result)
+
+        if method == "average":
+            result_max = pc.rank(
+                data,
+                sort_keys=sort_keys,
+                null_placement=null_placement,
+                tiebreaker="max",
+            )
+            result_max = result_max.cast(pa.float64())
+            result_min = result.cast(pa.float64())
+            result = pc.divide(pc.add(result_min, result_max), 2)
+
+        if pct:
+            if not pa.types.is_floating(result.type):
+                result = result.cast(pa.float64())
+            if method == "dense":
+                divisor = pc.max(result)
+            else:
+                divisor = pc.count(result)
+            result = pc.divide(result, divisor)
+
+        return type(self)(result)
 
     def _quantile(
         self: ArrowExtensionArrayT, qs: npt.NDArray[np.float64], interpolation: str

pandas/core/arrays/base.py

@@ -1576,8 +1576,6 @@ def _rank(
         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,

pandas/tests/series/methods/test_rank.py

@@ -11,6 +11,7 @@
 import pandas.util._test_decorators as td
 
 from pandas import (
+    NA,
     NaT,
     Series,
     Timestamp,
@@ -38,6 +39,21 @@ def results(request):
     return request.param
 
 
+@pytest.fixture(
+    params=[
+        "object",
+        "float64",
+        "int64",
+        "Float64",
+        "Int64",
+        pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow")),
+        pytest.param("int64[pyarrow]", marks=td.skip_if_no("pyarrow")),
+    ]
+)
+def dtype(request):
+    return request.param
+
+
 class TestSeriesRank:
     @td.skip_if_no_scipy
     def test_rank(self, datetime_series):
@@ -238,13 +254,28 @@ def test_rank_tie_methods(self, ser, results, dtype):
         [
             ("object", None, Infinity(), NegInfinity()),
             ("float64", np.nan, np.inf, -np.inf),
+            ("Float64", NA, np.inf, -np.inf),
+            pytest.param(
+                "float64[pyarrow]",
+                NA,
+                np.inf,
+                -np.inf,
+                marks=td.skip_if_no("pyarrow"),
+            ),
         ],
     )
     def test_rank_tie_methods_on_infs_nans(
         self, method, na_option, ascending, dtype, na_value, pos_inf, neg_inf
     ):
-        chunk = 3
+        if dtype == "float64[pyarrow]":
+            if method == "average":
+                exp_dtype = "float64[pyarrow]"
+            else:
+                exp_dtype = "uint64[pyarrow]"
+        else:
+            exp_dtype = "float64"
 
+        chunk = 3
         in_arr = [neg_inf] * chunk + [na_value] * chunk + [pos_inf] * chunk
         iseries = Series(in_arr, dtype=dtype)
         exp_ranks = {
@@ -264,7 +295,7 @@ def test_rank_tie_methods_on_infs_nans(
         expected = order if ascending else order[::-1]
         expected = list(chain.from_iterable(expected))
         result = iseries.rank(method=method, na_option=na_option, ascending=ascending)
-        tm.assert_series_equal(result, Series(expected, dtype="float64"))
+        tm.assert_series_equal(result, Series(expected, dtype=exp_dtype))
 
     def test_rank_desc_mix_nans_infs(self):
         # GH 19538
@@ -299,7 +330,6 @@ def test_rank_methods_series(self, method, op, value):
         expected = Series(sprank, index=index).astype("float64")
         tm.assert_series_equal(result, expected)
 
-    @pytest.mark.parametrize("dtype", ["O", "f8", "i8"])
     @pytest.mark.parametrize(
         "ser, exp",
         [
@@ -319,7 +349,6 @@ def test_rank_dense_method(self, dtype, ser, exp):
         expected = Series(exp).astype(result.dtype)
         tm.assert_series_equal(result, expected)
 
-    @pytest.mark.parametrize("dtype", ["O", "f8", "i8"])
     def test_rank_descending(self, ser, results, dtype):
         method, _ = results
         if "i" in dtype:
@@ -365,7 +394,6 @@ def test_rank_modify_inplace(self):
 # GH15630, pct should be on 100% basis when method='dense'
 
 
-@pytest.mark.parametrize("dtype", ["O", "f8", "i8"])
 @pytest.mark.parametrize(
     "ser, exp",
     [
@@ -387,7 +415,6 @@ def test_rank_dense_pct(dtype, ser, exp):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("dtype", ["O", "f8", "i8"])
 @pytest.mark.parametrize(
     "ser, exp",
     [
@@ -409,7 +436,6 @@ def test_rank_min_pct(dtype, ser, exp):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("dtype", ["O", "f8", "i8"])
 @pytest.mark.parametrize(
     "ser, exp",
     [
@@ -431,7 +457,6 @@ def test_rank_max_pct(dtype, ser, exp):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("dtype", ["O", "f8", "i8"])
 @pytest.mark.parametrize(
     "ser, exp",
     [
@@ -453,7 +478,6 @@ def test_rank_average_pct(dtype, ser, exp):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("dtype", ["f8", "i8"])
 @pytest.mark.parametrize(
     "ser, exp",
     [