Move rank to indexed_frame

python/cudf/cudf/core/frame.py

@@ -1288,92 +1288,6 @@ def _quantiles(
         result._copy_type_metadata(self)
         return result
 
-    @_cudf_nvtx_annotate
-    def rank(
-        self,
-        axis=0,
-        method="average",
-        numeric_only=None,
-        na_option="keep",
-        ascending=True,
-        pct=False,
-    ):
-        """
-        Compute numerical data ranks (1 through n) along axis.
-        By default, equal values are assigned a rank that is the average of the
-        ranks of those values.
-
-        Parameters
-        ----------
-        axis : {0 or 'index'}, default 0
-            Index to direct ranking.
-        method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
-            How to rank the group of records that have the same value
-            (i.e. ties):
-            * average: average rank of the group
-            * min: lowest rank in the group
-            * max: highest rank in the group
-            * first: ranks assigned in order they appear in the array
-            * dense: like 'min', but rank always increases by 1 between groups.
-        numeric_only : bool, optional
-            For DataFrame objects, rank only numeric columns if set to True.
-        na_option : {'keep', 'top', 'bottom'}, default 'keep'
-            How to rank NaN values:
-            * keep: assign NaN rank to NaN values
-            * top: assign smallest rank to NaN values if ascending
-            * bottom: assign highest rank to NaN values if ascending.
-        ascending : bool, default True
-            Whether or not the elements should be ranked in ascending order.
-        pct : bool, default False
-            Whether or not to display the returned rankings in percentile
-            form.
-
-        Returns
-        -------
-        same type as caller
-            Return a Series or DataFrame with data ranks as values.
-        """
-        if isinstance(self, cudf.BaseIndex):
-            warnings.warn(
-                "Index.rank is deprecated and will be removed.",
-                FutureWarning,
-            )
-
-        if method not in {"average", "min", "max", "first", "dense"}:
-            raise KeyError(method)
-
-        method_enum = libcudf.sort.RankMethod[method.upper()]
-        if na_option not in {"keep", "top", "bottom"}:
-            raise ValueError(
-                "na_option must be one of 'keep', 'top', or 'bottom'"
-            )
-
-        if axis not in (0, "index"):
-            raise NotImplementedError(
-                f"axis must be `0`/`index`, "
-                f"axis={axis} is not yet supported in rank"
-            )
-
-        source = self
-        if numeric_only:
-            numeric_cols = (
-                name
-                for name in self._data.names
-                if _is_non_decimal_numeric_dtype(self._data[name])
-            )
-            source = self._get_columns_by_label(numeric_cols)
-            if source.empty:
-                return source.astype("float64")
-
-        result_columns = libcudf.sort.rank_columns(
-            [*source._columns], method_enum, na_option, ascending, pct
-        )
-
-        return self.__class__._from_data(
-            dict(zip(source._column_names, result_columns)),
-            index=source._index,
-        ).astype(np.float64)
-
     @_cudf_nvtx_annotate
     def shift(self, periods=1, freq=None, axis=0, fill_value=None):
         """Shift values by `periods` positions."""

python/cudf/cudf/core/indexed_frame.py

@@ -3545,6 +3545,93 @@ def ge(
             other=other, op="__ge__", fill_value=fill_value, can_reindex=True
         )
 
+    @_cudf_nvtx_annotate
+    def rank(
+        self,
+        axis=0,
+        method="average",
+        numeric_only=None,
+        na_option="keep",
+        ascending=True,
+        pct=False,
+    ):
+        """
+        Compute numerical data ranks (1 through n) along axis.
+
+        By default, equal values are assigned a rank that is the average of the
+        ranks of those values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}, default 0
+            Index to direct ranking.
+        method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+            How to rank the group of records that have the same value
+            (i.e. ties):
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+            * first: ranks assigned in order they appear in the array
+            * dense: like 'min', but rank always increases by 1 between groups.
+        numeric_only : bool, optional
+            For DataFrame objects, rank only numeric columns if set to True.
+        na_option : {'keep', 'top', 'bottom'}, default 'keep'
+            How to rank NaN values:
+            * keep: assign NaN rank to NaN values
+            * top: assign smallest rank to NaN values if ascending
+            * bottom: assign highest rank to NaN values if ascending.
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+
+        Returns
+        -------
+        same type as caller
+            Return a Series or DataFrame with data ranks as values.
+        """
+        if isinstance(self, cudf.BaseIndex):
+            warnings.warn(
+                "Index.rank is deprecated and will be removed.",
+                FutureWarning,
+            )
+
+        if method not in {"average", "min", "max", "first", "dense"}:
+            raise KeyError(method)
+
+        method_enum = libcudf.sort.RankMethod[method.upper()]
+        if na_option not in {"keep", "top", "bottom"}:
+            raise ValueError(
+                "na_option must be one of 'keep', 'top', or 'bottom'"
+            )
+
+        if axis not in (0, "index"):
+            raise NotImplementedError(
+                f"axis must be `0`/`index`, "
+                f"axis={axis} is not yet supported in rank"
+            )
+
+        source = self
+        if numeric_only:
+            numeric_cols = (
+                name
+                for name in self._data.names
+                if _is_non_decimal_numeric_dtype(self._data[name])
+            )
+            source = self._get_columns_by_label(numeric_cols)
+            if source.empty:
+                return source.astype("float64")
+
+        result_columns = libcudf.sort.rank_columns(
+            [*source._columns], method_enum, na_option, ascending, pct
+        )
+
+        return self.__class__._from_data(
+            dict(zip(source._column_names, result_columns)),
+            index=source._index,
+        ).astype(np.float64)
+
 
 def _check_duplicate_level_names(specified, level_names):
     """Raise if any of `specified` has duplicates in `level_names`."""