adjusted window tests

Author: Sebastian Goldmann

pandas/_libs/window/aggregations.pyx

@@ -498,9 +498,9 @@ cdef float64_t calc_skew(int64_t minp, int64_t nobs,
         if nobs < 3:
             result = NaN
         # GH 42064 46431
-        # uniform case, force result to be 0
+        # uniform case, force result to be NaN
         elif num_consecutive_same_value >= nobs:
-            result = 0.0
+            result = NaN
         # #18044: with uniform distribution, floating issue will
         #         cause B != 0. and cause the result is a very
         #         large number.

pandas/tests/window/test_rolling_skew_kurt.py

@@ -170,7 +170,7 @@ def test_center_reindex_frame(frame, roll_func):
 
 
 def test_rolling_skew_edge_cases(step):
-    expected = Series([np.nan] * 4 + [0.0])[::step]
+    expected = Series([np.nan] * 5)[::step]
     # yields all NaN (0 variance)
     d = Series([1] * 5)
     x = d.rolling(window=5, step=step).skew()
@@ -191,7 +191,7 @@ def test_rolling_skew_edge_cases(step):
 
 
 def test_rolling_kurt_edge_cases(step):
-    expected = Series([np.nan] * 4 + [-3.0])[::step]
+    expected = Series([np.nan] * 5)[::step]
 
     # yields all NaN (0 variance)
     d = Series([1] * 5)
@@ -212,16 +212,14 @@ 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
+    # #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()
-    assert (a[a.index >= 9] == 0).all()
-    assert a[a.index < 9].isna().all()
+    assert a.isna().all()
 
 
 def test_rolling_kurt_eq_value_fperr(step):
     # #18804 all rolling kurt for all equal values should return Nan
     # #46717 update: all equal values should return -3 instead of NaN
     a = Series([1.1] * 15).rolling(window=10, step=step).kurt()
-    assert (a[a.index >= 9] == -3).all()
-    assert a[a.index < 9].isna().all()
+    assert a.isna().all()