Implemented review suggestions

Author: SoulofAkuma

pandas/_libs/window/aggregations.pyx

@@ -494,34 +494,18 @@ cdef float64_t calc_skew(int64_t minp, int64_t nobs,
                          int64_t num_consecutive_same_value
                          ) noexcept nogil:
     cdef:
-        float64_t result, dnobs, m2_cutoff
+        float64_t result, dnobs
         float64_t moments_ratio, correction
 
     if nobs >= minp:
         dnobs = <float64_t>nobs
 
-        # Relative cutoff as introduced in #62405
-        # See the comment in nanops.nankurt for further explanation
-        m2_cutoff = ((EpsF64 * mean) ** 2) * dnobs
-
         if nobs < 3:
             result = NaN
         # GH 42064 46431
         # uniform case, force result to be 0
         elif num_consecutive_same_value >= nobs:
             result = 0.0
-        # #18044: with degenerate distribution, floating issue will
-        #         cause m2 != 0. and cause the result is a very
-        #         large number.
-        #
-        #         in core/nanops.py nanskew/nankurt call the function
-        #         _zero_out_fperr(m2) to fix floating error.
-        #         if the variance is less than a relative cutoff value
-        #         it could be treated as zero, here we follow the original
-        #         skew/kurt behaviour to check
-        #         m2 <= ((float64_machine_eps * mean) ** 2) * observations
-        elif m2 <= m2_cutoff:
-            result = NaN
         else:
             moments_ratio = m3 / (m2 * sqrt(m2))
             correction = dnobs * sqrt((dnobs - 1)) / (dnobs - 2)

pandas/tests/window/conftest.py

@@ -109,16 +109,6 @@ def series():
     return series
 
 
-@pytest.fixture
-def low_variance_series():
-    """Make a mocked low variance series as a fixture"""
-    arr = np.random.default_rng(505).normal(loc=0e0, scale=1e-8, size=100)
-    locs = np.arange(20, 40)
-    arr[locs] = np.nan
-    series = Series(arr, index=bdate_range(datetime(2009, 1, 1), periods=100))
-    return series
-
-
 @pytest.fixture
 def frame():
     """Make mocked frame as fixture."""

pandas/tests/window/test_rolling_skew_kurt.py

@@ -1,3 +1,4 @@
+from datetime import datetime
 from functools import partial
 
 import numpy as np
@@ -6,6 +7,7 @@
 from pandas import (
     DataFrame,
     Series,
+    bdate_range,
     concat,
     isna,
     notna,
@@ -26,13 +28,21 @@ def test_series(series, sp_func, roll_func):
 
 
 @pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]])
-def test_low_variance_series(low_variance_series, sp_func, roll_func):
+def test_low_variance_series(sp_func, roll_func):
     sp_stats = pytest.importorskip("scipy.stats")
 
+    arr = np.random.default_rng(505).normal(loc=0e0, scale=1e-16, size=100)
+    locs = np.arange(20, 40)
+    arr[locs] = np.nan
+    low_variance_series = Series(
+        arr, index=bdate_range(datetime(2009, 1, 1), periods=100)
+    )
+
     compare_func = partial(getattr(sp_stats, sp_func), bias=False)
-    result = getattr(low_variance_series.rolling(50), roll_func)()
-    assert isinstance(result, Series)
-    tm.assert_almost_equal(result.iloc[-1], compare_func(low_variance_series[-50:]))
+    window = low_variance_series.rolling(50)
+    result = getattr(window, roll_func)()
+    expected = window.apply(compare_func)
+    tm.assert_almost_equal(result, expected)
 
 
 @pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]])