BUG: pd.crosstab(s1, s2) handle column index incorrectly when both series have tuple names

doc/source/whatsnew/v1.1.0.rst

@@ -141,6 +141,8 @@ Reshaping
 
 -
 - Bug in :meth:`DataFrame.pivot_table` when only MultiIndexed columns is set (:issue:`17038`)
+- Bug in :func:`crosstab` when inputs are two Series and have tuple names, the output will keep dummy MultiIndex as columns. (:issue:`18321`)
+
 
 Sparse
 ^^^^^^

pandas/core/reshape/pivot.py

@@ -581,6 +581,8 @@ def crosstab(
     from pandas import DataFrame
 
     df = DataFrame(data, index=common_idx)
+    original_df_cols = df.columns
+
     if values is None:
         df["__dummy__"] = 0
         kwargs = {"aggfunc": len, "fill_value": 0}
@@ -589,7 +591,7 @@ def crosstab(
         kwargs = {"aggfunc": aggfunc}
 
     table = df.pivot_table(
-        "__dummy__",
+        ["__dummy__"],
         index=rownames,
         columns=colnames,
         margins=margins,
@@ -598,6 +600,12 @@ def crosstab(
         **kwargs,
     )
 
+    # GH18321, after pivoting, an extra top level of column index of `__dummy__` is
+    # created, and this extra level should not be included in the further steps
+    if not table.empty:
+        cols_diff = df.columns.difference(original_df_cols)[0]
+        table = table[cols_diff]
+
     # Post-process
     if normalize is not False:
         table = _normalize(

pandas/tests/reshape/test_pivot.py

@@ -2557,6 +2557,19 @@ def test_crosstab_tuple_name(self, names):
         result = pd.crosstab(s1, s2)
         tm.assert_frame_equal(result, expected)
 
+    def test_crosstab_both_tuple_names(self):
+        # GH 18321
+        s1 = pd.Series(range(3), name=("a", "b"))
+        s2 = pd.Series(range(3), name=("c", "d"))
+
+        expected = pd.DataFrame(
+            np.eye(3, dtype="int64"),
+            index=pd.Index(range(3), name=("a", "b")),
+            columns=pd.Index(range(3), name=("c", "d")),
+        )
+        result = crosstab(s1, s2)
+        tm.assert_frame_equal(result, expected)
+
     def test_crosstab_unsorted_order(self):
         df = pd.DataFrame({"b": [3, 1, 2], "a": [5, 4, 6]}, index=["C", "A", "B"])
         result = pd.crosstab(df.index, [df.b, df.a])