Don't materialize column during RangeIndex methods

python/cudf/cudf/core/_base_index.py

@@ -517,7 +517,7 @@ def where(self, cond, other=None, inplace=False):
         """
         raise NotImplementedError
 
-    def factorize(self, sort=False, na_sentinel=None, use_na_sentinel=None):
+    def factorize(self, sort: bool = False, use_na_sentinel: bool = True):
         raise NotImplementedError
 
     def union(self, other, sort=None):
@@ -2061,7 +2061,8 @@ def dropna(self, how="any"):
             one null value. "all" drops only rows containing
             *all* null values.
         """
-
+        if not self.hasnans:
+            return self.copy()
         # This is to be consistent with IndexedFrame.dropna to handle nans
         # as nulls by default
         data_columns = [

python/cudf/cudf/core/index.py

@@ -21,6 +21,7 @@
 import cupy
 import numpy as np
 import pandas as pd
+import pyarrow as pa
 from typing_extensions import Self
 
 import cudf
@@ -255,6 +256,15 @@ def searchsorted(
         ), "Invalid ascending flag"
         return search_range(value, self.as_range, side=side)
 
+    def factorize(self, sort: bool = False, use_na_sentinel: bool = True):
+        if sort and self.step < 0:
+            codes = cupy.arange(len(self) - 1, -1, -1)
+            uniques = self[::-1]
+        else:
+            codes = cupy.arange(len(self), dtype=np.intp)
+            uniques = self
+        return codes, uniques
+
     @property  # type: ignore
     @_cudf_nvtx_annotate
     def name(self):
@@ -267,31 +277,31 @@ def name(self, value):
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def start(self):
+    def start(self) -> int:
         """
         The value of the `start` parameter (0 if this was not supplied).
         """
         return self._start
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def stop(self):
+    def stop(self) -> int:
         """
         The value of the stop parameter.
         """
         return self._stop
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def step(self):
+    def step(self) -> int:
         """
         The value of the step parameter.
         """
         return self._step
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def _num_rows(self):
+    def _num_rows(self) -> int:
         return len(self)
 
     @cached_property  # type: ignore
@@ -302,33 +312,33 @@ def _values(self):
         else:
             return column.column_empty(0, masked=False, dtype=self.dtype)
 
-    def _clean_nulls_from_index(self):
+    def _clean_nulls_from_index(self) -> Self:
         return self
 
-    def _is_numeric(self):
+    def _is_numeric(self) -> bool:
         return True
 
-    def _is_boolean(self):
+    def _is_boolean(self) -> bool:
         return False
 
-    def _is_integer(self):
+    def _is_integer(self) -> bool:
         return True
 
-    def _is_floating(self):
+    def _is_floating(self) -> bool:
         return False
 
-    def _is_object(self):
+    def _is_object(self) -> bool:
         return False
 
-    def _is_categorical(self):
+    def _is_categorical(self) -> bool:
         return False
 
-    def _is_interval(self):
+    def _is_interval(self) -> bool:
         return False
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def hasnans(self):
+    def hasnans(self) -> bool:
         return False
 
     @property  # type: ignore
@@ -378,12 +388,15 @@ def astype(self, dtype, copy: bool = True):
             return self
         return self._as_int_index().astype(dtype, copy=copy)
 
+    def fillna(self, value, downcast=None):
+        return self.copy()
+
     @_cudf_nvtx_annotate
     def drop_duplicates(self, keep="first"):
         return self
 
     @_cudf_nvtx_annotate
-    def duplicated(self, keep="first"):
+    def duplicated(self, keep="first") -> cupy.ndarray:
         return cupy.zeros(len(self), dtype=bool)
 
     @_cudf_nvtx_annotate
@@ -399,8 +412,13 @@ def __repr__(self):
             + ")"
         )
 
+    @property
+    @_cudf_nvtx_annotate
+    def size(self) -> int:
+        return len(self)
+
     @_cudf_nvtx_annotate
-    def __len__(self):
+    def __len__(self) -> int:
         return len(range(self._start, self._stop, self._step))
 
     @_cudf_nvtx_annotate
@@ -492,7 +510,7 @@ def to_pandas(
         )
 
     @property
-    def is_unique(self):
+    def is_unique(self) -> bool:
         return True
 
     @cached_property
@@ -501,24 +519,24 @@ def as_range(self):
 
     @cached_property  # type: ignore
     @_cudf_nvtx_annotate
-    def is_monotonic_increasing(self):
+    def is_monotonic_increasing(self) -> bool:
         return self._step > 0 or len(self) <= 1
 
     @cached_property  # type: ignore
     @_cudf_nvtx_annotate
-    def is_monotonic_decreasing(self):
+    def is_monotonic_decreasing(self) -> bool:
         return self._step < 0 or len(self) <= 1
 
     @_cudf_nvtx_annotate
-    def memory_usage(self, deep=False):
+    def memory_usage(self, deep: bool = False) -> int:
         if deep:
             warnings.warn(
                 "The deep parameter is ignored and is only included "
                 "for pandas compatibility."
             )
         return 0
 
-    def unique(self):
+    def unique(self) -> Self:
         # RangeIndex always has unique values
         return self
 
@@ -841,36 +859,40 @@ def _columns(self):
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def values_host(self):
-        return self.to_pandas().values
+    def values_host(self) -> np.ndarray:
+        return np.arange(start=self.start, stop=self.stop, step=self.step)
 
     @_cudf_nvtx_annotate
     def argsort(
         self,
         ascending=True,
         na_position="last",
-    ):
+    ) -> cupy.ndarray:
         if na_position not in {"first", "last"}:
             raise ValueError(f"invalid na_position: {na_position}")
 
-        indices = cupy.arange(0, len(self))
         if (ascending and self._step < 0) or (
             not ascending and self._step > 0
         ):
-            indices = indices[::-1]
-        return indices
+            return cupy.arange(len(self), -1, -1)
+        else:
+            return cupy.arange(len(self))
 
     @_cudf_nvtx_annotate
     def where(self, cond, other=None, inplace=False):
         return self._as_int_index().where(cond, other, inplace)
 
     @_cudf_nvtx_annotate
-    def to_numpy(self):
+    def to_numpy(self) -> np.ndarray:
         return self.values_host
 
     @_cudf_nvtx_annotate
-    def to_arrow(self):
-        return self._as_int_index().to_arrow()
+    def to_cupy(self) -> cupy.ndarray:
+        return self.values
+
+    @_cudf_nvtx_annotate
+    def to_arrow(self) -> pa.Array:
+        return pa.array(self._range)
 
     def __array__(self, dtype=None):
         raise TypeError(
@@ -881,17 +903,17 @@ def __array__(self, dtype=None):
         )
 
     @_cudf_nvtx_annotate
-    def nunique(self):
+    def nunique(self) -> int:
         return len(self)
 
     @_cudf_nvtx_annotate
-    def isna(self):
+    def isna(self) -> cupy.ndarray:
         return cupy.zeros(len(self), dtype=bool)
 
     isnull = isna
 
     @_cudf_nvtx_annotate
-    def notna(self):
+    def notna(self) -> cupy.ndarray:
         return cupy.ones(len(self), dtype=bool)
 
     notnull = isna
@@ -915,12 +937,15 @@ def max(self):
         return self._minmax("max")
 
     @property
-    def values(self):
+    def values(self) -> cupy.ndarray:
         return cupy.arange(self.start, self.stop, self.step)
 
-    def any(self):
+    def any(self) -> bool:
         return any(self._range)
 
+    def all(self) -> bool:
+        return 0 not in self._range
+
     def append(self, other):
         result = self._as_int_index().append(other)
         return self._try_reconstruct_range_index(result)
@@ -946,14 +971,20 @@ def isin(self, values):
 
         return self._values.isin(values).values
 
-    def __neg__(self):
-        return -self._as_int_index()
+    def __pos__(self) -> Self:
+        return self.copy()
 
-    def __pos__(self):
-        return +self._as_int_index()
+    def __neg__(self) -> Self:
+        rng = range(-self.start, -self.stop, -self.step)
+        return type(self)(rng, name=self.name)
 
-    def __abs__(self):
-        return abs(self._as_int_index())
+    def __abs__(self) -> Self | Index:
+        if len(self) == 0 or self.min() >= 0:
+            return self.copy()
+        elif self.max() <= 0:
+            return -self
+        else:
+            return abs(self._as_int_index())
 
     @_warn_no_dask_cudf
     def __dask_tokenize__(self):

python/cudf/cudf/tests/test_index.py

@@ -3176,3 +3176,26 @@ def test_index_to_pandas_arrow_type(scalar):
     result = idx.to_pandas(arrow_type=True)
     expected = pd.Index(pd.arrays.ArrowExtensionArray(pa_array))
     pd.testing.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("data", [range(-3, 3), range(1, 3), range(0)])
+def test_rangeindex_all(data):
+    result = cudf.RangeIndex(data).all()
+    expected = cudf.Index(list(data)).all()
+    assert result == expected
+
+
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("data", [range(2), range(2, -1, -1)])
+def test_rangeindex_factorize(sort, data):
+    res_codes, res_uniques = cudf.RangeIndex(data).factorize(sort=sort)
+    exp_codes, exp_uniques = cudf.Index(list(data)).factorize(sort=sort)
+    assert_eq(res_codes, exp_codes)
+    assert_eq(res_uniques, exp_uniques)
+
+
+def test_rangeindex_dropna():
+    ri = cudf.RangeIndex(range(2))
+    result = ri.dropna()
+    expected = ri.copy()
+    assert_eq(result, expected)