use MAXfloat64 MINfloat64 for inf; reformat code

Author: auderson

pandas/_libs/window/aggregations.pyx

@@ -310,7 +310,7 @@ cdef inline void add_var(float64_t val, float64_t *nobs, float64_t *mean_x,
     nobs[0] = nobs[0] + 1
 
     # GH#42064, record num of same values to remove floating point artifacts
-    if val == prev_value[0] and val != np.inf and val != np.NINF:
+    if val == prev_value[0] and val != MAXfloat64 and val != MINfloat64:
         num_consecutive_same_value[0] += 1
     else:
         num_consecutive_same_value[0] = 1  # reset to 1 (include current value itself)

pandas/core/_numba/kernels/var_.py

@@ -16,8 +16,13 @@
 
 @numba.jit(nopython=True, nogil=True, parallel=False)
 def add_var(
-    val: float, nobs: int, mean_x: float, ssqdm_x: float, compensation: float,
-    num_consecutive_same_value: int, prev_value: float
+    val: float,
+    nobs: int,
+    mean_x: float,
+    ssqdm_x: float,
+    compensation: float,
+    num_consecutive_same_value: int,
+    prev_value: float
 ) -> tuple[int, float, float, float, int, float]:
     if not np.isnan(val):
 
@@ -93,11 +98,22 @@ def sliding_var(
 
             for j in range(s, e):
                 val = values[j]
-                nobs, mean_x, ssqdm_x, compensation_add, \
-                    num_consecutive_same_value, prev_value = add_var(
-                        val, nobs, mean_x, ssqdm_x, compensation_add,
-                        num_consecutive_same_value, prev_value
-                    )
+                (
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_var(
+                    val,
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
         else:
             for j in range(start[i - 1], s):
                 val = values[j]
@@ -107,11 +123,22 @@ def sliding_var(
 
             for j in range(end[i - 1], e):
                 val = values[j]
-                nobs, mean_x, ssqdm_x, compensation_add, \
-                    num_consecutive_same_value, prev_value = add_var(
-                        val, nobs, mean_x, ssqdm_x, compensation_add,
-                        num_consecutive_same_value, prev_value
-                    )
+                (
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                ) = add_var(
+                    val,
+                    nobs,
+                    mean_x,
+                    ssqdm_x,
+                    compensation_add,
+                    num_consecutive_same_value,
+                    prev_value,
+                )
 
         if nobs >= min_periods and nobs > ddof:
             if nobs == 1 or num_consecutive_same_value >= nobs: