test(rolling): undo test modifications for degenerate windows

Author: Alvaro-Kothe

pandas/tests/window/test_rolling.py

@@ -1843,17 +1843,16 @@ def test_rolling_mean_sum_floating_artifacts():
     assert (result[-3:] == 0).all()
 
 
-@pytest.mark.xfail(reason="Series.rolling.kurt computes -3 to degenerate distribution")
 def test_rolling_skew_kurt_floating_artifacts():
     # GH 42064 46431
 
     sr = Series([1 / 3, 4, 0, 0, 0, 0, 0])
     r = sr.rolling(4)
     result = r.skew()
-    expected = Series([np.nan, np.nan, np.nan, 1.9619045191072484, 2.0, np.nan, np.nan])
+    expected = Series([np.nan, np.nan, np.nan, 1.9619045191072484, 2.0, 0.0, 0.0])
     tm.assert_series_equal(result, expected)
     result = r.kurt()
-    expected = Series([np.nan, np.nan, np.nan, 3.8636048803878786, 4.0, np.nan, np.nan])
+    expected = Series([np.nan, np.nan, np.nan, 3.8636048803878786, 4.0, -3.0, -3.0])
     tm.assert_series_equal(result, expected)
 
 

pandas/tests/window/test_rolling_skew_kurt.py

@@ -170,11 +170,11 @@ def test_center_reindex_frame(frame, roll_func):
 
 
 def test_rolling_skew_edge_cases(step):
-    expected = Series([np.nan] * 5)[::step]
+    expected = Series([np.nan] * 4 + [0.0])[::step]
     # yields all NaN (0 variance)
     d = Series([1] * 5)
     x = d.rolling(window=5, step=step).skew()
-    # index 4 should be NaN as it contains 5 same obs (variance 0)
+    # index 4 should be 0 as it contains 5 same obs
     tm.assert_series_equal(expected, x)
 
     expected = Series([np.nan] * 5)[::step]
@@ -213,9 +213,10 @@ def test_rolling_kurt_edge_cases(step):
 
 def test_rolling_skew_eq_value_fperr(step):
     # #18804 all rolling skew for all equal values should return Nan
+    # #46717 update: all equal values should return 0 instead of NaN
     a = Series([1.1] * 15).rolling(window=10, step=step).skew()
-    expected = Series([np.nan] * 15)[::step]
-    tm.assert_series_equal(expected, a)
+    assert (a[a.index >= 9] == 0).all()
+    assert a[a.index < 9].isna().all()
 
 
 def test_rolling_kurt_eq_value_fperr(step):