BUG: pivot_table with overlapping values

doc/source/whatsnew/v3.0.0.rst

@@ -835,6 +835,7 @@ Reshaping
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when ``sort=False`` (:issue:`54987`, :issue:`55516`)
 - Bug in :meth:`DataFrame.merge` when merging two :class:`DataFrame` on ``intc`` or ``uintc`` types on Windows (:issue:`60091`, :issue:`58713`)
 - Bug in :meth:`DataFrame.pivot_table` incorrectly subaggregating results when called without an ``index`` argument (:issue:`58722`)
+- Bug in :meth:`DataFrame.pivot_table` incorrectly ignoring the ``values`` argument when also supplied to the ``index`` or ``columns`` parameters (:issue:`57876`, :issue:`61292`)
 - Bug in :meth:`DataFrame.stack` with the new implementation where ``ValueError`` is raised when ``level=[]`` (:issue:`60740`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)
 - Bug in :meth:`concat` where concatenating DataFrame and Series with ``ignore_index = True`` drops the series name (:issue:`60723`, :issue:`56257`)

pandas/core/reshape/pivot.py

@@ -336,6 +336,11 @@ def __internal_pivot_table(
         values = list(values)
 
     grouped = data.groupby(keys, observed=observed, sort=sort, dropna=dropna)
+    if values_passed:
+        # GH#57876 and GH#61292
+        # mypy is not aware `grouped[values]` will always be a DataFrameGroupBy
+        grouped = grouped[values]  # type: ignore[assignment]
+
     agged = grouped.agg(aggfunc, **kwargs)
 
     if dropna and isinstance(agged, ABCDataFrame) and len(agged.columns):

pandas/tests/reshape/test_pivot.py

@@ -2554,6 +2554,39 @@ def test_pivot_table_index_and_column_keys_with_nan(self, dropna):
 
         tm.assert_frame_equal(left=result, right=expected)
 
+    @pytest.mark.parametrize(
+        "index, columns",
+        [("Category", "Value"), ("Value", "Category")],
+        ids=["values-and-columns", "values-and-index"],
+    )
+    def test_pivot_table_values_as_two_params(self, index, columns, request):
+        # GH#57876
+        data = {"Category": ["A", "B", "A", "B"], "Value": [10, 20, 40, 50]}
+        df = DataFrame(data)
+        result = df.pivot_table(
+            index=index, columns=columns, values="Value", aggfunc="count"
+        )
+        nan = np.nan
+        cat_index = Index(data=["A", "B"], name="Category")
+        val_index = Index(data=[10, 20, 40, 50], name="Value")
+        if request.node.callspec.id == "values-and-columns":
+            e_data = [
+                [1.0, nan, 1.0, nan],
+                [nan, 1.0, nan, 1.0],
+            ]
+            expected = DataFrame(data=e_data, index=cat_index, columns=val_index)
+
+        else:
+            e_data = [
+                [1.0, nan],
+                [nan, 1.0],
+                [1.0, nan],
+                [nan, 1.0],
+            ]
+            expected = DataFrame(data=e_data, index=val_index, columns=cat_index)
+
+        tm.assert_frame_equal(result, expected)
+
 
 class TestPivot:
     def test_pivot(self):

pandas/tests/reshape/test_pivot_multilevel.py

@@ -250,3 +250,45 @@ def test_pivot_df_multiindex_index_none():
         columns=Index(["label1", "label2"], name="label"),
     )
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "index, columns",
+    [("index", ["col", "value"]), (["index", "value"], "col")],
+    ids=["values-and-columns", "values-and-index"],
+)
+def test_pivot_table_multiindex_values_as_two_params(index, columns, request):
+    # GH#61292
+    data = [
+        ["A", 1, 50, -1],
+        ["B", 1, 100, -2],
+        ["A", 2, 100, -2],
+        ["B", 2, 200, -4],
+    ]
+    df = pd.DataFrame(data=data, columns=["index", "col", "value", "extra"])
+    result = df.pivot_table(values="value", index=index, columns=columns)
+    nan = np.nan
+    if request.node.callspec.id == "values-and-columns":
+        e_data = [
+            [50.0, nan, 100.0, nan],
+            [nan, 100.0, nan, 200.0],
+        ]
+        e_index = Index(data=["A", "B"], name="index")
+        e_cols = MultiIndex.from_arrays(
+            arrays=[[1, 1, 2, 2], [50, 100, 100, 200]], names=["col", "value"]
+        )
+
+    else:
+        e_data = [
+            [50.0, nan],
+            [nan, 100.0],
+            [100.0, nan],
+            [nan, 200.0],
+        ]
+        e_index = MultiIndex.from_arrays(
+            arrays=[["A", "A", "B", "B"], [50, 100, 100, 200]], names=["index", "value"]
+        )
+        e_cols = Index(data=[1, 2], name="col")
+
+    expected = pd.DataFrame(data=e_data, index=e_index, columns=e_cols)
+    tm.assert_frame_equal(result, expected)