fix groupby value_counts

pandas/tests/groupby/test_value_counts.py

@@ -860,7 +860,7 @@ def test_mixed_groupings(normalize, expected_label, expected_values):
     result = gp.value_counts(sort=True, normalize=normalize)
     expected = DataFrame(
         {
-            "level_0": [4, 4, 5],
+            "level_0": np.array([4, 4, 5], dtype=np.int_),
             "A": [1, 1, 2],
             "level_2": [8, 8, 7],
             "B": [1, 3, 2],
@@ -883,7 +883,8 @@ def test_column_label_duplicates(test, columns, expected_names, as_index):
     # Test for duplicate input column labels and generated duplicate labels
     df = DataFrame([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]], columns=columns)
     expected_data = [(1, 0, 7, 3, 5, 9), (2, 1, 8, 4, 6, 10)]
-    result = df.groupby(["a", [0, 1], "d"], as_index=as_index).value_counts()
+    keys = ["a", np.array([0, 1], dtype=np.int64), "d"]
+    result = df.groupby(keys, as_index=as_index).value_counts()
     if as_index:
         expected = Series(
             data=(1, 1),
@@ -922,7 +923,7 @@ def test_result_label_duplicates(normalize, expected_label):
 def test_ambiguous_grouping():
     # Test that groupby is not confused by groupings length equal to row count
     df = DataFrame({"a": [1, 1]})
-    gb = df.groupby([1, 1])
+    gb = df.groupby(np.array([1, 1], dtype=np.int64))
     result = gb.value_counts()
     expected = Series([2], index=MultiIndex.from_tuples([[1, 1]], names=[None, "a"]))
     tm.assert_series_equal(result, expected)