CLN: use fused type for lib.has_infs

pandas/_libs/lib.pyi

@@ -219,8 +219,7 @@ def array_equivalent_object(
     left: np.ndarray,  # object[:]
     right: np.ndarray,  # object[:]
 ) -> bool: ...
-def has_infs_f8(arr: np.ndarray) -> bool: ...  # const float64_t[:]
-def has_infs_f4(arr: np.ndarray) -> bool: ...  # const float32_t[:]
+def has_infs(arr: np.ndarray) -> bool: ...  # const floating[:]
 def get_reverse_indexer(
     indexer: np.ndarray,  # const intp_t[:]
     length: int,

pandas/_libs/lib.pyx

@@ -25,6 +25,7 @@ from cpython.tuple cimport (
     PyTuple_New,
     PyTuple_SET_ITEM,
 )
+from cython cimport floating
 
 PyDateTime_IMPORT
 
@@ -519,36 +520,22 @@ def get_reverse_indexer(const intp_t[:] indexer, Py_ssize_t length) -> ndarray:
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def has_infs_f4(const float32_t[:] arr) -> bool:
+# Can add const once https://github.com/cython/cython/issues/1772 resolved
+def has_infs(floating[:] arr) -> bool:
     cdef:
         Py_ssize_t i, n = len(arr)
-        float32_t inf, neginf, val
+        floating inf, neginf, val
+        bint ret = False
 
     inf = np.inf
     neginf = -inf
-
-    for i in range(n):
-        val = arr[i]
-        if val == inf or val == neginf:
-            return True
-    return False
-
-
-@cython.wraparound(False)
-@cython.boundscheck(False)
-def has_infs_f8(const float64_t[:] arr) -> bool:
-    cdef:
-        Py_ssize_t i, n = len(arr)
-        float64_t inf, neginf, val
-
-    inf = np.inf
-    neginf = -inf
-
-    for i in range(n):
-        val = arr[i]
-        if val == inf or val == neginf:
-            return True
-    return False
+    with nogil:
+        for i in range(n):
+            val = arr[i]
+            if val == inf or val == neginf:
+                ret = True
+                break
+    return ret
 
 
 def maybe_indices_to_slice(ndarray[intp_t] indices, int max_len):

pandas/core/nanops.py

@@ -177,10 +177,8 @@ def _bn_ok_dtype(dtype: DtypeObj, name: str) -> bool:
 
 def _has_infs(result) -> bool:
     if isinstance(result, np.ndarray):
-        if result.dtype == "f8":
-            return lib.has_infs_f8(result.ravel("K"))
-        elif result.dtype == "f4":
-            return lib.has_infs_f4(result.ravel("K"))
+        if result.dtype == "f8" or result.dtype == "f4":
+            return lib.has_infs(result.ravel("K"))
     try:
         return np.isinf(result).any()
     except (TypeError, NotImplementedError):