Add DataFrame.pivot_table. (#12015)

This PR adds the method `DataFrame.pivot_table` to enhance pandas API compatibility. It uses the exact same arguments as `cudf.pivot_table` but automatically supplies the first argument (a DataFrame).

Related: #11314

Authors:
  - Bradley Dice (https://github.com/bdice)

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

URL: #12015

docs/cudf/source/api_docs/dataframe.rst

@@ -210,6 +210,7 @@ Reshaping, sorting, transposing
    DataFrame.interleave_columns
    DataFrame.partition_by_hash
    DataFrame.pivot
+   DataFrame.pivot_table
    DataFrame.scatter_by_map
    DataFrame.sort_values
    DataFrame.sort_index

python/cudf/cudf/core/dataframe.py

@@ -6407,11 +6407,39 @@ def append(
     @_cudf_nvtx_annotate
     @copy_docstring(reshape.pivot)
     def pivot(self, index, columns, values=None):
-
         return cudf.core.reshape.pivot(
             self, index=index, columns=columns, values=values
         )
 
+    @_cudf_nvtx_annotate
+    @copy_docstring(reshape.pivot_table)
+    def pivot_table(
+        self,
+        values=None,
+        index=None,
+        columns=None,
+        aggfunc="mean",
+        fill_value=None,
+        margins=False,
+        dropna=None,
+        margins_name="All",
+        observed=False,
+        sort=True,
+    ):
+        return cudf.core.reshape.pivot_table(
+            self,
+            values=values,
+            index=index,
+            columns=columns,
+            aggfunc=aggfunc,
+            fill_value=fill_value,
+            margins=margins,
+            dropna=dropna,
+            margins_name=margins_name,
+            observed=observed,
+            sort=sort,
+        )
+
     @_cudf_nvtx_annotate
     @copy_docstring(reshape.unstack)
     def unstack(self, level=-1, fill_value=None):

python/cudf/cudf/tests/test_reshape.py

@@ -596,6 +596,42 @@ def test_pivot_table_simple(data, aggfunc, fill_value):
     assert_eq(expected, actual, check_dtype=False)
 
 
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "A": ["one", "one", "two", "three"] * 6,
+            "B": ["A", "B", "C"] * 8,
+            "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
+            "D": np.random.randn(24),
+            "E": np.random.randn(24),
+        }
+    ],
+)
+@pytest.mark.parametrize(
+    "aggfunc", ["mean", "count", {"D": "sum", "E": "count"}]
+)
+@pytest.mark.parametrize("fill_value", [0])
+def test_dataframe_pivot_table_simple(data, aggfunc, fill_value):
+    pdf = pd.DataFrame(data)
+    expected = pdf.pivot_table(
+        values=["D", "E"],
+        index=["A", "B"],
+        columns=["C"],
+        aggfunc=aggfunc,
+        fill_value=fill_value,
+    )
+    cdf = cudf.DataFrame(data)
+    actual = cdf.pivot_table(
+        values=["D", "E"],
+        index=["A", "B"],
+        columns=["C"],
+        aggfunc=aggfunc,
+        fill_value=fill_value,
+    )
+    assert_eq(expected, actual, check_dtype=False)
+
+
 def test_crosstab_simple():
     a = np.array(
         [