Consolidate remaining DataFrame/Series APIs (#11315)

This PR resolves #9038. Not all the remaining functions could be consolidated (for various reasons), but the combination was explored and where necessary the reasons not to combine the functions was documented in that issue. Some additional cleanup that was done during the exploration is also included in this PR, but ultimately `groupby` is the only method that was moved up to `IndexedFrame`.

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

Approvers:
  - Matthew Roeschke (https://github.com/mroeschke)

URL: #11315

python/cudf/cudf/core/dataframe.py

@@ -9,6 +9,7 @@
 import pickle
 import re
 import sys
+import textwrap
 import warnings
 from collections import abc, defaultdict
 from typing import (
@@ -66,7 +67,7 @@
     concat_columns,
 )
 from cudf.core.column_accessor import ColumnAccessor
-from cudf.core.groupby.groupby import DataFrameGroupBy
+from cudf.core.groupby.groupby import DataFrameGroupBy, groupby_doc_template
 from cudf.core.index import (
     BaseIndex,
     Index,
@@ -616,6 +617,8 @@ class DataFrame(IndexedFrame, Serializable, GetAttrGetItemMixin):
     _accessors: Set[Any] = set()
     _loc_indexer_type = _DataFrameLocIndexer
     _iloc_indexer_type = _DataFrameIlocIndexer
+    _groupby = DataFrameGroupBy
+    _resampler = DataFrameResampler
 
     @_cudf_nvtx_annotate
     def __init__(
@@ -3824,7 +3827,19 @@ def join(
         return df
 
     @_cudf_nvtx_annotate
-    @copy_docstring(DataFrameGroupBy)
+    @docutils.doc_apply(
+        groupby_doc_template.format(
+            ret=textwrap.dedent(
+                """
+                Returns
+                -------
+                DataFrameGroupBy
+                    Returns a DataFrameGroupBy object that contains
+                    information about the groups.
+                """
+            )
+        )
+    )
     def groupby(
         self,
         by=None,
@@ -3837,40 +3852,16 @@ def groupby(
         observed=False,
         dropna=True,
     ):
-        if axis not in (0, "index"):
-            raise NotImplementedError("axis parameter is not yet implemented")
-
-        if group_keys is not True:
-            raise NotImplementedError(
-                "The group_keys keyword is not yet implemented"
-            )
-
-        if squeeze is not False:
-            raise NotImplementedError(
-                "squeeze parameter is not yet implemented"
-            )
-
-        if observed is not False:
-            raise NotImplementedError(
-                "observed parameter is not yet implemented"
-            )
-
-        if by is None and level is None:
-            raise TypeError(
-                "groupby() requires either by or level to be specified."
-            )
-
-        return (
-            DataFrameResampler(self, by=by)
-            if isinstance(by, cudf.Grouper) and by.freq
-            else DataFrameGroupBy(
-                self,
-                by=by,
-                level=level,
-                as_index=as_index,
-                dropna=dropna,
-                sort=sort,
-            )
+        return super().groupby(
+            by,
+            axis,
+            level,
+            as_index,
+            sort,
+            group_keys,
+            squeeze,
+            observed,
+            dropna,
         )
 
     def query(self, expr, local_dict=None):
@@ -5217,12 +5208,13 @@ def quantile(
             If q is a float, a Series will be returned where the index is
             the columns of self and the values are the quantiles.
 
-        Notes
-        -----
-        One notable difference from Pandas is when DataFrame is of
-        non-numeric types and result is expected to be a Series in case of
-        Pandas. cuDF will return a DataFrame as it doesn't support mixed
-        types under Series.
+        .. pandas-compat::
+            **DataFrame.quantile**
+
+            One notable difference from Pandas is when DataFrame is of
+            non-numeric types and result is expected to be a Series in case of
+            Pandas. cuDF will return a DataFrame as it doesn't support mixed
+            types under Series.
 
         Examples
         --------
@@ -5248,47 +5240,41 @@ def quantile(
         if axis not in (0, None):
             raise NotImplementedError("axis is not implemented yet")
 
+        data_df = self
         if numeric_only:
-            data_df = self.select_dtypes(
+            data_df = data_df.select_dtypes(
                 include=[np.number], exclude=["datetime64", "timedelta64"]
             )
-        else:
-            data_df = self
 
         if columns is None:
             columns = data_df._data.names
 
-        result = DataFrame()
-
+        # Ensure that qs is non-scalar so that we always get a column back.
+        qs = [q] if is_scalar(q) else q
+        result = {}
         for k in data_df._data.names:
-
             if k in columns:
-                res = data_df[k].quantile(
-                    q,
+                ser = data_df[k]
+                res = ser.quantile(
+                    qs,
                     interpolation=interpolation,
                     exact=exact,
                     quant_index=False,
-                )
-                if (
-                    not isinstance(
-                        res, (numbers.Number, pd.Timestamp, pd.Timedelta)
-                    )
-                    and len(res) == 0
-                ):
+                )._column
+                if len(res) == 0:
                     res = column.column_empty_like(
-                        q, dtype=data_df[k].dtype, masked=True, newsize=len(q)
+                        qs, dtype=ser.dtype, masked=True, newsize=len(qs)
                     )
-                result[k] = column.as_column(res)
+                result[k] = res
 
+        result = DataFrame._from_data(result)
         if isinstance(q, numbers.Number) and numeric_only:
-            result = result.fillna(np.nan)
-            result = result.iloc[0]
+            result = result.fillna(np.nan).iloc[0]
             result.index = data_df._data.to_pandas_index()
             result.name = q
             return result
         else:
-            q = list(map(float, [q] if isinstance(q, numbers.Number) else q))
-            result.index = q
+            result.index = list(map(float, qs))
             return result
 
     @_cudf_nvtx_annotate