TST: Add tests for MultiIndex columns cases in aggregate relabelling

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -495,6 +495,70 @@ def test_mangled(self):
         tm.assert_frame_equal(result, expected)
 
 
+def test_agg_relabel_multiindex_column():
+    # GH 29422, add tests for multiindex column cases
+    df = DataFrame(
+        {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
+    )
+    df.columns = pd.MultiIndex.from_tuples([("x", "group"), ("y", "A"), ("y", "B")])
+    idx = pd.Index(["a", "b"], name=("x", "group"))
+
+    result = df.groupby(("x", "group")).agg(a_max=(("y", "A"), "max"))
+    expected = DataFrame({"a_max": [1, 3]}, index=idx)
+    tm.assert_frame_equal(result, expected)
+
+    # multiple columns, and different agg methods
+    result = df.groupby(("x", "group")).agg(
+        a_max=(("y", "A"), "max"),
+        a_min=(("y", "A"), np.min),
+        b_mean=(("y", "B"), "mean"),
+    )
+    expected = DataFrame(
+        {"a_max": [1, 3], "a_min": [0, 2], "b_mean": [5.5, 7.5]}, index=idx
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # multiple colums, with lamdba being used
+    result = df.groupby(("x", "group")).agg(
+        a_max=(("y", "A"), lambda x: max(x)),
+        a_const=(("y", "A"), lambda x: 1),
+        b_mean=(("y", "B"), "mean"),
+    )
+    expected = DataFrame(
+        {"a_max": [1, 3], "a_const": [1, 1], "b_mean": [5.5, 7.5]}, index=idx
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_relabel_multiindex_raises():
+    # GH 29422, add tests for raises senarios in multiindex column cases
+    df = DataFrame(
+        {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
+    )
+    df.columns = pd.MultiIndex.from_tuples([("x", "group"), ("y", "A"), ("y", "B")])
+
+    with pytest.raises(KeyError, match="does not exist"):
+        df.groupby(("x", "group")).agg(a=(("Y", "a"), "max"))
+
+    with pytest.raises(SpecificationError, match="Function names"):
+        df.groupby(("x", "group")).agg(a=(("y", "A"), "min"), b=(("y", "A"), "min"))
+
+
+def test_named_agg_multiindex_column():
+    # GH 29422, add tests for namedagg in multiindex column cases
+    df = DataFrame(
+        {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
+    )
+    df.columns = pd.MultiIndex.from_tuples([("x", "group"), ("y", "A"), ("y", "B")])
+    idx = pd.Index(["a", "b"], name=("x", "group"))
+
+    result = df.groupby(("x", "group")).agg(
+        a_max=pd.NamedAgg(("y", "A"), "max"), b_mean=pd.NamedAgg(("y", "B"), np.mean)
+    )
+    expected = DataFrame({"a_max": [1, 3], "b_mean": [5.5, 7.5]}, index=idx)
+    tm.assert_frame_equal(result, expected)
+
+
 def myfunc(s):
     return np.percentile(s, q=0.90)