From 4c3035fa57b9c1a9d7f347e6f442bdc5d2299f2a Mon Sep 17 00:00:00 2001
From: Fabrizio Primerano <fabri.96p@gmail.com>
Date: Sun, 23 Apr 2023 21:19:26 +0200
Subject: [PATCH] TST: Grouping with categorical interval columns (#52818)

---
 pandas/tests/groupby/test_groupby.py | 42 ++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index 53148eb37e15ad..cd33f031720e18 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -16,6 +16,7 @@
     DataFrame,
     Grouper,
     Index,
+    Interval,
     MultiIndex,
     RangeIndex,
     Series,
@@ -2972,6 +2973,47 @@ def test_groupby_numeric_only_std_no_result(numeric_only):
             dfgb.std(numeric_only=numeric_only)
 
 
+def test_grouping_with_categorical_interval_columns():
+    # GH#34164
+    df = DataFrame({"x": [0.1, 0.2, 0.3, -0.4, 0.5], "w": ["a", "b", "a", "c", "a"]})
+    qq = pd.qcut(df["x"], q=np.linspace(0, 1, 5))
+    result = df.groupby([qq, "w"], observed=False)["x"].agg("mean")
+    categorical_index_level_1 = Categorical(
+        [
+            Interval(-0.401, 0.1, closed="right"),
+            Interval(0.1, 0.2, closed="right"),
+            Interval(0.2, 0.3, closed="right"),
+            Interval(0.3, 0.5, closed="right"),
+        ],
+        ordered=True,
+    )
+    index_level_2 = ["a", "b", "c"]
+    mi = MultiIndex.from_product(
+        [categorical_index_level_1, index_level_2], names=["x", "w"]
+    )
+    expected = Series(
+        np.array(
+            [
+                0.1,
+                np.nan,
+                -0.4,
+                np.nan,
+                0.2,
+                np.nan,
+                0.3,
+                np.nan,
+                np.nan,
+                0.5,
+                np.nan,
+                np.nan,
+            ]
+        ),
+        index=mi,
+        name="x",
+    )
+    tm.assert_series_equal(result, expected)
+
+
 @pytest.mark.parametrize("bug_var", [1, "a"])
 def test_groupby_sum_on_nan_should_return_nan(bug_var):
     # GH 24196