Fix pivot index bug

doc/source/whatsnew/v1.2.0.rst

@@ -553,6 +553,7 @@ Reshaping
 - Bug in :meth:`DataFrame.agg` with ``func={'name':<FUNC>}`` incorrectly raising ``TypeError`` when ``DataFrame.columns==['Name']`` (:issue:`36212`)
 - Bug in :meth:`Series.transform` would give incorrect results or raise when the argument ``func`` was dictionary (:issue:`35811`)
 - Bug in :meth:`DataFrame.pivot` did not preserve :class:`MultiIndex` level names for columns when rows and columns both multiindexed (:issue:`36360`)
+- Bug in :meth:``DataFrame.pivot`` modified caller's ``index`` parameter when ``columns`` was passed but ``values`` was not (:issue:`37635`)
 - Bug in :func:`join` returned a non deterministic level-order for the resulting :class:`MultiIndex` (:issue:`36910`)
 - Bug in :meth:`DataFrame.combine_first()` caused wrong alignment with dtype ``string`` and one level of ``MultiIndex`` containing only ``NA`` (:issue:`37591`)
 - Fixed regression in :func:`merge` on merging DatetimeIndex with empty DataFrame (:issue:`36895`)

pandas/core/reshape/pivot.py

@@ -447,13 +447,12 @@ def pivot(
 
     if values is None:
         if index is not None:
-            cols = com.convert_to_list_like(index)
+            index = com.convert_to_list_like(index)
         else:
-            cols = []
-        cols.extend(columns)
+            index = []
 
         append = index is None
-        indexed = data.set_index(cols, append=append)
+        indexed = data.set_index(index + columns, append=append)
     else:
         if index is None:
             index = [Series(data.index, name=data.index.name)]

pandas/tests/reshape/test_pivot.py

@@ -2153,3 +2153,20 @@ def test_pivot_index_none(self):
 
         expected.columns.name = "columns"
         tm.assert_frame_equal(result, expected)
+
+    def test_pivot_index_list_values_none(self):
+        # Tests when index is a list and values None. See GH#37635
+        data = {
+            "index": ["A", "B", "C", "C", "B", "A"],
+            "columns": ["One", "One", "One", "Two", "Two", "Two"],
+            "values": [1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
+        }
+        index = ["index"]
+        columns = ["columns"]
+        frame = DataFrame(data)
+        frame.pivot(index=index, columns=columns, values=None)
+
+        expected_ser = Series(["index"])
+        result_ser = Series(index)
+
+        tm.assert_series_equal(result_ser, expected_ser)