part1: Simplify BaseIndex to an abstract class

python/cudf/benchmarks/API/bench_rangeindex.py

@@ -40,3 +40,8 @@ def bench_min(benchmark, rangeindex):
 def bench_where(benchmark, rangeindex):
     cond = rangeindex % 2 == 0
     benchmark(rangeindex.where, cond, 0)
+
+
+def bench_isin(benchmark, rangeindex):
+    values = [10, 100]
+    benchmark(rangeindex.isin, values)

python/cudf/cudf/core/_base_index.py

@@ -27,10 +27,7 @@
 from cudf.core.column import ColumnBase, column
 from cudf.core.column_accessor import ColumnAccessor
 from cudf.utils import ioutils
-from cudf.utils.dtypes import (
-    is_mixed_with_object_dtype,
-    numeric_normalize_types,
-)
+from cudf.utils.dtypes import is_mixed_with_object_dtype
 
 _index_astype_docstring = """\
 Create an Index with values cast to dtypes.
@@ -90,7 +87,7 @@ def size(self):
 
     @property
     def values(self):
-        return self._values.values
+        raise NotImplementedError
 
     def get_loc(self, key, method=None, tolerance=None):
         raise NotImplementedError
@@ -178,23 +175,6 @@ def names(self, values):
 
         self.name = values[0]
 
-    def _clean_nulls_from_index(self):
-        """
-        Convert all na values(if any) in Index object
-        to `<NA>` as a preprocessing step to `__repr__` methods.
-
-        This will involve changing type of Index object
-        to StringIndex but it is the responsibility of the `__repr__`
-        methods using this method to replace or handle representation
-        of the actual types correctly.
-        """
-        if self._values.has_nulls():
-            return cudf.Index(
-                self._values.astype("str").fillna(cudf._NA_REP), name=self.name
-            )
-        else:
-            return self
-
     @property
     def is_monotonic(self):
         """Return boolean if values in the object are monotonic_increasing.
@@ -549,13 +529,11 @@ def to_frame(self, index=True, name=None):
             Set the index of the returned DataFrame as the original Index
         name : str, default None
             Name to be used for the column
-
         Returns
         -------
         DataFrame
             cudf DataFrame
         """
-
         if name is not None:
             col_name = name
         elif self.name is None:
@@ -570,7 +548,7 @@ def any(self):
         """
         Return whether any elements is True in Index.
         """
-        return self._values.any()
+        raise NotImplementedError
 
     def to_pandas(self):
         """
@@ -589,7 +567,81 @@ def to_pandas(self):
         >>> type(idx)
         <class 'cudf.core.index.Int64Index'>
         """
-        return pd.Index(self._values.to_pandas(), name=self.name)
+        raise NotImplementedError
+
+    def isin(self, values):
+        """Return a boolean array where the index values are in values.
+
+        Compute boolean array of whether each index value is found in
+        the passed set of values. The length of the returned boolean
+        array matches the length of the index.
+
+        Parameters
+        ----------
+        values : set, list-like, Index
+            Sought values.
+
+        Returns
+        -------
+        is_contained : cupy array
+            CuPy array of boolean values.
+
+        Examples
+        --------
+        >>> idx = cudf.Index([1,2,3])
+        >>> idx
+        Int64Index([1, 2, 3], dtype='int64')
+
+        Check whether each index value in a list of values.
+
+        >>> idx.isin([1, 4])
+        array([ True, False, False])
+        """
+        # To match pandas behavior, even though only list-like objects are
+        # supposed to be passed, only scalars throw errors. Other types (like
+        # dicts) just transparently return False (see the implementation of
+        # ColumnBase.isin).
+        if is_scalar(values):
+            raise TypeError(
+                "only list-like objects are allowed to be passed "
+                f"to isin(), you passed a {type(values).__name__}"
+            )
+
+        return self._values.isin(values).values
+
+    def unique(self):
+        """
+        Return unique values in the index.
+
+        Returns
+        -------
+        Index without duplicates
+        """
+        raise NotImplementedError
+
+    def to_series(self, index=None, name=None):
+        """
+        Create a Series with both index and values equal to the index keys.
+        Useful with map for returning an indexer based on an index.
+
+        Parameters
+        ----------
+        index : Index, optional
+            Index of resulting Series. If None, defaults to original index.
+        name : str, optional
+            Name of resulting Series. If None, defaults to name of original
+            index.
+
+        Returns
+        -------
+        Series
+            The dtype will be based on the type of the Index values.
+        """
+        return cudf.Series._from_data(
+            self._data,
+            index=self.copy(deep=False) if index is None else index,
+            name=self.name if name is None else name,
+        )
 
     @ioutils.doc_to_dlpack()
     def to_dlpack(self):
@@ -599,7 +651,7 @@ def to_dlpack(self):
 
     def append(self, other):
         """
-        Append a collection of Index options together.
+        Append a collection of Index objects together.
 
         Parameters
         ----------
@@ -626,45 +678,7 @@ def append(self, other):
         >>> idx.append([other, other])
         Int64Index([1, 2, 10, 100, 200, 400, 50, 200, 400, 50], dtype='int64')
         """
-
-        if is_list_like(other):
-            to_concat = [self]
-            to_concat.extend(other)
-        else:
-            this = self
-            if len(other) == 0:
-                # short-circuit and return a copy
-                to_concat = [self]
-
-            other = cudf.Index(other)
-
-            if len(self) == 0:
-                to_concat = [other]
-
-            if len(self) and len(other):
-                if is_mixed_with_object_dtype(this, other):
-                    got_dtype = (
-                        other.dtype
-                        if this.dtype == cudf.dtype("object")
-                        else this.dtype
-                    )
-                    raise TypeError(
-                        f"cudf does not support appending an Index of "
-                        f"dtype `{cudf.dtype('object')}` with an Index "
-                        f"of dtype `{got_dtype}`, please type-cast "
-                        f"either one of them to same dtypes."
-                    )
-
-                if isinstance(self._values, cudf.core.column.NumericalColumn):
-                    if self.dtype != other.dtype:
-                        this, other = numeric_normalize_types(self, other)
-                to_concat = [this, other]
-
-        for obj in to_concat:
-            if not isinstance(obj, BaseIndex):
-                raise TypeError("all inputs must be Index")
-
-        return self._concat(to_concat)
+        raise NotImplementedError
 
     def difference(self, other, sort=None):
         """
@@ -1119,18 +1133,6 @@ def sort_values(
         else:
             return index_sorted
 
-    def unique(self):
-        """
-        Return unique values in the index.
-
-        Returns
-        -------
-        Index without duplicates
-        """
-        return cudf.core.index._index_from_data(
-            {self.name: self._values.unique()}, name=self.name
-        )
-
     def join(
         self, other, how="left", level=None, return_indexers=False, sort=False
     ):
@@ -1263,30 +1265,6 @@ def rename(self, name, inplace=False):
             out.name = name
             return out
 
-    def to_series(self, index=None, name=None):
-        """
-        Create a Series with both index and values equal to the index keys.
-        Useful with map for returning an indexer based on an index.
-
-        Parameters
-        ----------
-        index : Index, optional
-            Index of resulting Series. If None, defaults to original index.
-        name : str, optional
-            Dame of resulting Series. If None, defaults to name of original
-            index.
-
-        Returns
-        -------
-        Series
-            The dtype will be based on the type of the Index values.
-        """
-        return cudf.Series(
-            self._values,
-            index=self.copy(deep=False) if index is None else index,
-            name=self.name if name is None else name,
-        )
-
     def get_slice_bound(self, label, side, kind=None):
         """
         Calculate slice bound that corresponds to given label.
@@ -1339,47 +1317,6 @@ def __array_function__(self, func, types, args, kwargs):
         else:
             return NotImplemented
 
-    def isin(self, values):
-        """Return a boolean array where the index values are in values.
-
-        Compute boolean array of whether each index value is found in
-        the passed set of values. The length of the returned boolean
-        array matches the length of the index.
-
-        Parameters
-        ----------
-        values : set, list-like, Index
-            Sought values.
-
-        Returns
-        -------
-        is_contained : cupy array
-            CuPy array of boolean values.
-
-        Examples
-        --------
-        >>> idx = cudf.Index([1,2,3])
-        >>> idx
-        Int64Index([1, 2, 3], dtype='int64')
-
-        Check whether each index value in a list of values.
-
-        >>> idx.isin([1, 4])
-        array([ True, False, False])
-        """
-
-        # To match pandas behavior, even though only list-like objects are
-        # supposed to be passed, only scalars throw errors. Other types (like
-        # dicts) just transparently return False (see the implementation of
-        # ColumnBase.isin).
-        if is_scalar(values):
-            raise TypeError(
-                "only list-like objects are allowed to be passed "
-                f"to isin(), you passed a {type(values).__name__}"
-            )
-
-        return self._values.isin(values).values
-
     @classmethod
     def from_pandas(cls, index, nan_as_null=None):
         """

python/cudf/cudf/core/column/categorical.py

@@ -104,7 +104,7 @@ def __init__(self, parent: SeriesOrSingleColumnIndex):
         super().__init__(parent=parent)
 
     @property
-    def categories(self) -> "cudf.core.index.BaseIndex":
+    def categories(self) -> "cudf.core.index.GenericIndex":
         """
         The categories of this categorical.
         """