BUG: SeriesGroupBy.value_counts index name missing

Issue  pandas-dev#44324

Author: NumberPiOso

doc/source/whatsnew/v1.5.0.rst

@@ -220,6 +220,7 @@ Datetimelike
 - Bug in :meth:`DataFrame.quantile` with datetime-like dtypes and no rows incorrectly returning ``float64`` dtype instead of retaining datetime-like dtype (:issue:`41544`)
 - Bug in :func:`to_datetime` with sequences of ``np.str_`` objects incorrectly raising (:issue:`32264`)
 - Bug in :class:`Timestamp` construction when passing datetime components as positional arguments and ``tzinfo`` as a keyword argument incorrectly raising (:issue:`31929`)
+- Bug in :meth:`SeriesGroupBy.value_counts` index when passing categorical column (:issue:`44324`)
 -
 
 Timedelta

pandas/core/groupby/generic.py

@@ -602,14 +602,18 @@ def value_counts(
         ids, _, _ = self.grouper.group_info
         val = self.obj._values
 
+        names = self.grouper.names + [self.obj.name]
+
         def apply_series_value_counts():
-            return self.apply(
+            s = self.apply(
                 Series.value_counts,
                 normalize=normalize,
                 sort=sort,
                 ascending=ascending,
                 bins=bins,
             )
+            s.index.names = names
+            return s
 
         if bins is not None:
             if not np.iterable(bins):
@@ -683,7 +687,6 @@ def apply_series_value_counts():
         levels = [ping.group_index for ping in self.grouper.groupings] + [
             lev  # type: ignore[list-item]
         ]
-        names = self.grouper.names + [self.obj.name]
 
         if dropna:
             mask = codes[-1] != -1

pandas/tests/groupby/test_value_counts.py

@@ -22,6 +22,26 @@
 import pandas._testing as tm
 
 
+def tests_value_counts_index_names_category_column():
+    # GH44324 Missing name of index category column
+    df = DataFrame(
+        {
+            "gender": ["female"],
+            "country": ["US"],
+        }
+    )
+    df["gender"] = df["gender"].astype("category")
+    result = df.groupby("country")["gender"].value_counts()
+
+    # Construct expected, very specific multiindex
+    df_mi_expected = DataFrame([["US", "female"]], columns=["country", "gender"])
+    df_mi_expected["gender"] = df_mi_expected["gender"].astype("category")
+    mi_expected = MultiIndex.from_frame(df_mi_expected)
+    expected = Series([1], index=mi_expected, name="gender")
+
+    tm.assert_series_equal(result, expected)
+
+
 # our starting frame
 def seed_df(seed_nans, n, m):
     np.random.seed(1234)