pre-commit checks I forgot to run :/

Author: eicchen

pandas/_libs/window/aggregations.pyx

@@ -1,18 +1,17 @@
 # cython: boundscheck=False, wraparound=False, cdivision=True
 from libc.math cimport (
+    abs,
+    isfinite,
+    log10,
+    pow,
     round,
     signbit,
     sqrt,
-    pow,
-    log10,
-    abs,
-    isfinite,
 )
+from libcpp cimport bool
 from libcpp.deque cimport deque
 from libcpp.stack cimport stack
 from libcpp.unordered_map cimport unordered_map
-from libcpp cimport bool
-
 
 from pandas._libs.algos cimport TiebreakEnumType
 
@@ -26,8 +25,6 @@ from numpy cimport (
     ndarray,
 )
 
-
-
 cnp.import_array()
 
 import cython
@@ -731,41 +728,44 @@ cdef float64_t calc_kurt(int64_t minp, int64_t nobs,
 
     return result
 
-cdef void update_sum_of_window( float64_t val,
-                                float64_t **x_value,
-                                float64_t **comp_value,
-                                int power_of_element,  
-                                bool add_mode,   #1 for add_kurt, 0 for remove_kurt
-                                ) noexcept nogil:
+cdef void update_sum_of_window(float64_t val,
+                               float64_t **x_value,
+                               float64_t **comp_value,
+                               int power_of_element,
+                               bool add_mode,   # 1 for add_kurt, 0 for remove_kurt
+                               ) noexcept nogil:
 
     cdef:
-        float64_t val_raised, new_sum
+        float64_t val_raised
+        int  val_length, x_length
         bool val_length_flag, x_length_flag
-    
+
     if add_mode:
         val_raised = pow(val, power_of_element)
     else:
         val_raised = -pow(val, power_of_element)
 
-    x_length_flag = abs(log10(abs(x_value[0][0]))) > 15 and isfinite(abs(log10(abs(x_value[0][0])))) == 1
-    val_length_flag = abs(log10(abs(val_raised))) > 15 and isfinite(abs(log10(abs(val_raised)))) == 1
+    x_length = abs(log10(abs(x_value[0][0])))
+    val_length = abs(log10(abs(val_raised)))
 
-    # We'll try to maintain comp_value as the counter for 
+    x_length_flag = x_length > 15 and isfinite(x_length)
+    val_length_flag = val_length > 15 and isfinite(val_length)
+
+    # We'll try to maintain comp_value as the counter for
     # numbers <1e15 to keep it from getting rounded out.
     if x_length_flag and val_length_flag:
-        #Both > 1e15 or < 1-e15
+        # Both > 1e15 or < 1-e15
         x_value[0][0] += val_raised
 
     elif x_length_flag:
         comp_value[0][0] += val_raised
 
-
     elif val_length_flag:
         comp_value[0][0] += x_value[0][0]
         x_value[0][0] = val_raised
-        
+
     else:
-        #Neither are >1e15/<1e-15, safe to proceed
+        # Neither are >1e15/<1e-15, safe to proceed
         x_value[0][0] += val_raised
 
         if comp_value[0][0] != 0:
@@ -819,6 +819,7 @@ cdef void remove_kurt(float64_t val, int64_t *nobs,
         update_sum_of_window(val, &xxx,  &compensation_xxx,  3, 0)
         update_sum_of_window(val, &xxxx, &compensation_xxxx, 4, 0)
 
+
 def roll_kurt(ndarray[float64_t] values, ndarray[int64_t] start,
               ndarray[int64_t] end, int64_t minp) -> np.ndarray:
     cdef:

pandas/tests/window/test_rolling_skew_kurt.py

@@ -226,52 +226,60 @@ def test_rolling_kurt_eq_value_fperr(step):
     assert (a[a.index >= 9] == -3).all()
     assert a[a.index < 9].isna().all()
 
-@pytest.mark.parametrize("test_len, window_size, modifiers",
-                         [([0, 10], 5,  [[0,1e6], [3, -1e6]]),
-                          ([0, 10], 5,  [[0,1e-6], [3, 1e6]]),
-                          ([10, 100], 20,[[40, -1e10], [59, -9e9]]),
-                          ([10500, 11000], 200,[[10581, 0], [109900, -1e6], [10999, 0]]),
-                          ]
-                        )
+
+@pytest.mark.parametrize(
+    "test_len, window_size, modifiers",
+    [
+        ([0, 10], 5, [[0, 1e6], [3, -1e6]]),
+        ([0, 10], 5, [[0, 1e-6], [3, 1e6]]),
+        ([10, 100], 20, [[40, -1e10], [59, -9e9]]),
+        ([10500, 11000], 200, [[10581, 0], [109900, -1e6], [10999, 0]]),
+    ],
+)
 def test_rolling_kurt_outlier_influence(test_len, window_size, modifiers):
     # #61416 Extreme values causes kurtosis value to become incorrect
-    test_series = Series(range(test_len[0], test_len[1]), index = range(test_len[0], test_len[1]))
+    test_series = Series(
+        range(test_len[0], test_len[1]), index=range(test_len[0], test_len[1])
+    )
     for ind, number in modifiers:
         test_series = test_series.replace(ind, number)
-        
-    #minimum elements needed for "window_size" number of kurts 
+
+    # minimum elements needed for "window_size" number of kurts
     test_len_diff = test_len[1] - test_len[0]
-    min_elements_needed = test_len_diff - 2*window_size + 1 
-    expected_series = (test_series[min_elements_needed:].reindex(range(test_len[0], test_len[1])))
-    
-    actual = test_series.rolling(window_size,min_periods=1).kurt()
-    expected = expected_series.rolling(window_size,min_periods=1).kurt()
-    
-    tm.assert_series_equal(actual.tail(window_size), 
-                           expected.tail(window_size)
-                        )
-    
-@pytest.mark.parametrize("array_param, window_size, modifiers",
-                         [([10, 10, 10], 5,  [[0,1e6], [3, -1e6]]),
-                          ([-15, 10, 10], 5,  [[0,1e2], [3, 1e6]]),
-                          ([1e4, 1e3, 100], 20, [[90,-1e7], [0, 1e7]]),
-                          ([1e-3, 3e-3, 100], 20, [[90,100], [20, 1e4]]),
-                          ]
-                        )
+    min_elements_needed = test_len_diff - 2 * window_size + 1
+    expected_series = test_series[min_elements_needed:].reindex(
+        range(test_len[0], test_len[1])
+    )
+
+    actual = test_series.rolling(window_size, min_periods=1).kurt()
+    expected = expected_series.rolling(window_size, min_periods=1).kurt()
+
+    tm.assert_series_equal(actual.tail(window_size), expected.tail(window_size))
+
+
+@pytest.mark.parametrize(
+    "array_param, window_size, modifiers",
+    [
+        ([10, 10, 10], 5, [[0, 1e6], [3, -1e6]]),
+        ([-15, 10, 10], 5, [[0, 1e2], [3, 1e6]]),
+        ([1e4, 1e3, 100], 20, [[90, -1e7], [0, 1e7]]),
+        ([1e-3, 3e-3, 100], 20, [[90, 100], [20, 1e4]]),
+    ],
+)
 def test_rolling_kurt_outlier_influence_rand(array_param, window_size, modifiers):
     # #61416 Extreme values causes kurtosis value to become incorrect
-    rand_array = np.random.default_rng(5).normal(array_param[0], array_param[1], array_param[2])
+    rand_array = np.random.default_rng(5).normal(
+        array_param[0], array_param[1], array_param[2]
+    )
     test_series = Series(rand_array)
     for ind, number in modifiers:
         test_series = test_series.replace(ind, number)
-        
-    #minimum elements needed for "window_size" number of kurts 
-    min_elements_needed = array_param[2] - 2*window_size + 1 
-    expected_series = (test_series[min_elements_needed:])
-        
-    actual = test_series.rolling(window_size,min_periods=1).kurt()
-    expected = expected_series.rolling(window_size,min_periods=1).kurt()
-    
-    tm.assert_series_equal(actual.tail(window_size), 
-                           expected.tail(window_size)
-                        )
+
+    # minimum elements needed for "window_size" number of kurts
+    min_elements_needed = array_param[2] - 2 * window_size + 1
+    expected_series = test_series[min_elements_needed:]
+
+    actual = test_series.rolling(window_size, min_periods=1).kurt()
+    expected = expected_series.rolling(window_size, min_periods=1).kurt()
+
+    tm.assert_series_equal(actual.tail(window_size), expected.tail(window_size))