PERF: RangeIndex.value_counts/searchsorted/to_numpy (pandas-dev#58376)

* Add RangeIndex.value_counts,searchsorted,to_numpy

* Undo engine stuff

* Finish searchsorted, add wahtsnew

* Remove old to_numpy implementation

* Add whatsnew for to_numpy

* add whatsnew number

* Fix typing

Author: mroeschke

doc/source/whatsnew/v3.0.0.rst

@@ -340,6 +340,9 @@ Performance improvements
 - Performance improvement in :meth:`RangeIndex.argmin` and :meth:`RangeIndex.argmax` (:issue:`57823`)
 - Performance improvement in :meth:`RangeIndex.insert` returning a :class:`RangeIndex` instead of a :class:`Index` when the :class:`RangeIndex` is empty. (:issue:`57833`)
 - Performance improvement in :meth:`RangeIndex.round` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57824`)
+- Performance improvement in :meth:`RangeIndex.searchsorted` (:issue:`58376`)
+- Performance improvement in :meth:`RangeIndex.to_numpy` when specifying an ``na_value`` (:issue:`58376`)
+- Performance improvement in :meth:`RangeIndex.value_counts` (:issue:`58376`)
 - Performance improvement in :meth:`RangeIndex.join` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57651`, :issue:`57752`)
 - Performance improvement in :meth:`RangeIndex.reindex` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57647`, :issue:`57752`)
 - Performance improvement in :meth:`RangeIndex.take` returning a :class:`RangeIndex` instead of a :class:`Index` when possible. (:issue:`57445`, :issue:`57752`)

pandas/core/base.py

@@ -556,7 +556,6 @@ def array(self) -> ExtensionArray:
         """
         raise AbstractMethodError(self)
 
-    @final
     def to_numpy(
         self,
         dtype: npt.DTypeLike | None = None,
@@ -668,7 +667,7 @@ def to_numpy(
         )
 
         values = self._values
-        if fillna:
+        if fillna and self.hasnans:
             if not can_hold_element(values, na_value):
                 # if we can't hold the na_value asarray either makes a copy or we
                 # error before modifying values. The asarray later on thus won't make
@@ -943,7 +942,6 @@ def _map_values(self, mapper, na_action=None):
 
         return algorithms.map_array(arr, mapper, na_action=na_action)
 
-    @final
     def value_counts(
         self,
         normalize: bool = False,

pandas/core/indexes/range.py

@@ -57,9 +57,13 @@
         Dtype,
         JoinHow,
         NaPosition,
+        NumpySorter,
         Self,
         npt,
     )
+
+    from pandas import Series
+
 _empty_range = range(0)
 _dtype_int64 = np.dtype(np.int64)
 
@@ -1359,3 +1363,64 @@ def take(  # type: ignore[override]
                 taken += self.start
 
         return self._shallow_copy(taken, name=self.name)
+
+    def value_counts(
+        self,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        bins=None,
+        dropna: bool = True,
+    ) -> Series:
+        from pandas import Series
+
+        if bins is not None:
+            return super().value_counts(
+                normalize=normalize,
+                sort=sort,
+                ascending=ascending,
+                bins=bins,
+                dropna=dropna,
+            )
+        name = "proportion" if normalize else "count"
+        data: npt.NDArray[np.floating] | npt.NDArray[np.signedinteger] = np.ones(
+            len(self), dtype=np.int64
+        )
+        if normalize:
+            data = data / len(self)
+        return Series(data, index=self.copy(), name=name)
+
+    def searchsorted(  # type: ignore[override]
+        self,
+        value,
+        side: Literal["left", "right"] = "left",
+        sorter: NumpySorter | None = None,
+    ) -> npt.NDArray[np.intp] | np.intp:
+        if side not in {"left", "right"} or sorter is not None:
+            return super().searchsorted(value=value, side=side, sorter=sorter)
+
+        was_scalar = False
+        if is_scalar(value):
+            was_scalar = True
+            array_value = np.array([value])
+        else:
+            array_value = np.asarray(value)
+        if array_value.dtype.kind not in "iu":
+            return super().searchsorted(value=value, side=side, sorter=sorter)
+
+        if flip := (self.step < 0):
+            rng = self._range[::-1]
+            start = rng.start
+            step = rng.step
+            shift = side == "right"
+        else:
+            start = self.start
+            step = self.step
+            shift = side == "left"
+        result = (array_value - start - int(shift)) // step + 1
+        if flip:
+            result = len(self) - result
+        result = np.maximum(np.minimum(result, len(self)), 0)
+        if was_scalar:
+            return np.intp(result.item())
+        return result.astype(np.intp, copy=False)

pandas/tests/indexes/ranges/test_range.py

@@ -874,3 +874,36 @@ def test_getitem_integers_return_index():
     result = RangeIndex(0, 10, 2, name="foo")[[0, 1, -1]]
     expected = Index([0, 2, 8], dtype="int64", name="foo")
     tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize(
+    "rng",
+    [
+        range(3),
+        range(0),
+        range(0, 3, 2),
+        range(3, -3, -2),
+    ],
+)
+def test_value_counts(sort, dropna, ascending, normalize, rng):
+    ri = RangeIndex(rng, name="A")
+    result = ri.value_counts(
+        normalize=normalize, sort=sort, ascending=ascending, dropna=dropna
+    )
+    expected = Index(list(rng), name="A").value_counts(
+        normalize=normalize, sort=sort, ascending=ascending, dropna=dropna
+    )
+    tm.assert_series_equal(result, expected, check_index_type=False)
+
+
+@pytest.mark.parametrize("side", ["left", "right"])
+@pytest.mark.parametrize("value", [0, -5, 5, -3, np.array([-5, -3, 0, 5])])
+def test_searchsorted(side, value):
+    ri = RangeIndex(-3, 3, 2)
+    result = ri.searchsorted(value=value, side=side)
+    expected = Index(list(ri)).searchsorted(value=value, side=side)
+    if isinstance(value, int):
+        assert result == expected
+    else:
+        tm.assert_numpy_array_equal(result, expected)