Backport PR pandas-dev#55327: COMPAT: Fix warning with numba >= 0.58.0

doc/source/whatsnew/v2.2.0.rst

@@ -944,6 +944,7 @@ Other
 - Bug in rendering a :class:`Series` with a :class:`MultiIndex` when one of the index level's names is 0 not having that name displayed (:issue:`55415`)
 - Bug in the error message when assigning an empty :class:`DataFrame` to a column (:issue:`55956`)
 - Bug when time-like strings were being cast to :class:`ArrowDtype` with ``pyarrow.time64`` type (:issue:`56463`)
+- Fixed a spurious deprecation warning from ``numba`` >= 0.58.0 when passing a numpy ufunc in :class:`pandas.core.window.Rolling.apply` with ``engine="numba"`` (:issue:`55247`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_220.contributors:

pandas/core/util/numba_.py

@@ -1,11 +1,14 @@
 """Common utilities for Numba operations"""
 from __future__ import annotations
 
+import types
 from typing import (
     TYPE_CHECKING,
     Callable,
 )
 
+import numpy as np
+
 from pandas.compat._optional import import_optional_dependency
 from pandas.errors import NumbaUtilError
 
@@ -83,6 +86,12 @@ def jit_user_function(func: Callable) -> Callable:
     if numba.extending.is_jitted(func):
         # Don't jit a user passed jitted function
         numba_func = func
+    elif getattr(np, func.__name__, False) is func or isinstance(
+        func, types.BuiltinFunctionType
+    ):
+        # Not necessary to jit builtins or np functions
+        # This will mess up register_jitable
+        numba_func = func
     else:
         numba_func = numba.extending.register_jitable(func)
 

pandas/tests/window/test_numba.py

@@ -446,3 +446,10 @@ def test_table_method_ewm(self, data, method, axis, nogil, parallel, nopython):
             engine_kwargs=engine_kwargs, engine="numba"
         )
         tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numba")
+def test_npfunc_no_warnings():
+    df = DataFrame({"col1": [1, 2, 3, 4, 5]})
+    with tm.assert_produces_warning(False):
+        df.col1.rolling(2).apply(np.prod, raw=True, engine="numba")