Move more methods into SingleColumnFrame (#8253)

Continuation of #8115 and #8166. Moves more logic out of the Index/Series classes into the new common parent class to reduce code duplication and ensure feature parity.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - https://github.com/brandon-b-miller
  - Michael Wang (https://github.com/isVoid)

URL: #8253

python/cudf/cudf/core/frame.py

@@ -28,8 +28,8 @@
 from cudf.utils.dtypes import (
     is_categorical_dtype,
     is_column_like,
-    is_numerical_dtype,
     is_decimal_dtype,
+    is_numerical_dtype,
     is_scalar,
     min_scalar_type,
 )
@@ -3364,6 +3364,10 @@ def __bool__(self):
             "a.empty, a.bool(), a.item(), a.any() or a.all()."
         )
 
+    @property
+    def _num_columns(self):
+        return 1
+
     @property
     def _column(self):
         return self._data[self.name]
@@ -3529,6 +3533,79 @@ def to_arrow(self):
         """
         return self._column.to_arrow()
 
+    @property
+    def is_unique(self):
+        """Return boolean if values in the object are unique.
+
+        Returns
+        -------
+        bool
+        """
+        return self._column.is_unique
+
+    @property
+    def is_monotonic(self):
+        """Return boolean if values in the object are monotonic_increasing.
+
+        This property is an alias for :attr:`is_monotonic_increasing`.
+
+        Returns
+        -------
+        bool
+        """
+        return self.is_monotonic_increasing
+
+    @property
+    def is_monotonic_increasing(self):
+        """Return boolean if values in the object are monotonic_increasing.
+
+        Returns
+        -------
+        bool
+        """
+        return self._column.is_monotonic_increasing
+
+    @property
+    def is_monotonic_decreasing(self):
+        """Return boolean if values in the object are monotonic_decreasing.
+
+        Returns
+        -------
+        bool
+        """
+        return self._column.is_monotonic_decreasing
+
+    @property
+    def __cuda_array_interface__(self):
+        return self._column.__cuda_array_interface__
+
+    def factorize(self, na_sentinel=-1):
+        """Encode the input values as integer labels
+
+        Parameters
+        ----------
+        na_sentinel : number
+            Value to indicate missing category.
+
+        Returns
+        --------
+        (labels, cats) : (cupy.ndarray, cupy.ndarray or Index)
+            - *labels* contains the encoded values
+            - *cats* contains the categories in order that the N-th
+              item corresponds to the (N-1) code.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['a', 'a', 'c'])
+        >>> codes, uniques = s.factorize()
+        >>> codes
+        array([0, 0, 1], dtype=int8)
+        >>> uniques
+        StringIndex(['a' 'c'], dtype='object')
+        """
+        return cudf.core.algorithms.factorize(self, na_sentinel=na_sentinel)
+
     @property
     def _copy_construct_defaults(self):
         """A default dictionary of kwargs to be used for copy construction."""

python/cudf/cudf/core/index.py

@@ -336,17 +336,6 @@ def _clean_nulls_from_index(self):
         else:
             return self
 
-    def factorize(self, na_sentinel=-1):
-        """
-        Encode the input values as integer labels
-
-        See Also
-        --------
-        cudf.core.series.Series.factorize : Encode the input values of Series.
-
-        """
-        return cudf.core.algorithms.factorize(self, na_sentinel=na_sentinel)
-
     @property
     def nlevels(self):
         """
@@ -1144,59 +1133,6 @@ def to_series(self, index=None, name=None):
             name=self.name if name is None else name,
         )
 
-    @property
-    def is_unique(self):
-        """
-        Return if the index has unique values.
-        """
-        raise (NotImplementedError)
-
-    @property
-    def is_monotonic(self):
-        """
-        Alias for is_monotonic_increasing.
-        """
-        return self.is_monotonic_increasing
-
-    @property
-    def is_monotonic_increasing(self):
-        """
-        Return if the index is monotonic increasing
-        (only equal or increasing) values.
-        """
-        return self._values.is_monotonic_increasing
-
-    @property
-    def is_monotonic_decreasing(self):
-        """
-        Return if the index is monotonic decreasing
-        (only equal or decreasing) values.
-        """
-        return self._values.is_monotonic_decreasing
-
-    @property
-    def empty(self):
-        """
-        Indicator whether Index is empty.
-
-        True if Index is entirely empty (no items).
-
-        Returns
-        -------
-        out : bool
-            If Index is empty, return True, if not return False.
-
-        Examples
-        --------
-        >>> import cudf
-        >>> index = cudf.Index([])
-        >>> index
-        Float64Index([], dtype='float64')
-        >>> index.empty
-        True
-        """
-        return not self.size
-
     def get_slice_bound(self, label, side, kind):
         """
         Calculate slice bound that corresponds to given label.
@@ -1278,9 +1214,7 @@ def isin(self, values):
         array([ True, False, False])
         """
 
-        result = self.to_series().isin(values).values
-
-        return result
+        return self._values.isin(values).values
 
     def where(self, cond, other=None):
         """
@@ -1312,10 +1246,6 @@ def where(self, cond, other=None):
         """
         return super().where(cond=cond, other=other)
 
-    @property
-    def __cuda_array_interface__(self):
-        raise (NotImplementedError)
-
     def memory_usage(self, deep=False):
         """
         Memory usage of the values.
@@ -1508,10 +1438,6 @@ def step(self):
         """
         return self._step
 
-    @property
-    def _num_columns(self):
-        return 1
-
     @property
     def _num_rows(self):
         return len(self)
@@ -1773,10 +1699,6 @@ def get_slice_bound(self, label, side, kind=None):
         pos = search_range(start, stop, label, step, side=side)
         return pos
 
-    @property
-    def __cuda_array_interface__(self):
-        return self._values.__cuda_array_interface__
-
     def memory_usage(self, **kwargs):
         return 0
 
@@ -1838,7 +1760,7 @@ def _initialize(self, values, **kwargs):
 
     @property
     def _values(self):
-        return next(iter(self._data.columns))
+        return self._column
 
     def copy(self, name=None, deep=False, dtype=None, names=None):
         """
@@ -1990,20 +1912,9 @@ def find_label_range(self, first, last):
             end += 1
         return begin, end
 
-    @property
-    def is_unique(self):
-        """
-        Return if the index has unique values.
-        """
-        return self._values.is_unique
-
     def get_slice_bound(self, label, side, kind):
         return self._values.get_slice_bound(label, side, kind)
 
-    @property
-    def __cuda_array_interface__(self):
-        return self._values.__cuda_array_interface__
-
 
 class NumericIndex(GenericIndex):
     """Immutable, ordered and sliceable sequence of labels.

python/cudf/cudf/core/multiindex.py

@@ -204,6 +204,11 @@ def names(self, value):
             )
         self._names = pd.core.indexes.frozen.FrozenList(value)
 
+    @property
+    def _num_columns(self):
+        # MultiIndex is not a single-columned frame.
+        return super(SingleColumnFrame, self)._num_columns
+
     def rename(self, names, inplace=False):
         """
         Alter MultiIndex level names