ENH: Add numba engine to several rolling aggregations

asv_bench/benchmarks/rolling.py

@@ -50,20 +50,24 @@ class Engine:
         ["int", "float"],
         [np.sum, lambda x: np.sum(x) + 5],
         ["cython", "numba"],
+        ["sum", "max", "min", "median", "mean"],
     )
-    param_names = ["constructor", "dtype", "function", "engine"]
+    param_names = ["constructor", "dtype", "function", "engine", "method"]
 
-    def setup(self, constructor, dtype, function, engine):
+    def setup(self, constructor, dtype, function, engine, method):
         N = 10 ** 3
         arr = (100 * np.random.random(N)).astype(dtype)
         self.data = getattr(pd, constructor)(arr)
 
-    def time_rolling_apply(self, constructor, dtype, function, engine):
+    def time_rolling_apply(self, constructor, dtype, function, engine, method):
         self.data.rolling(10).apply(function, raw=True, engine=engine)
 
-    def time_expanding_apply(self, constructor, dtype, function, engine):
+    def time_expanding_apply(self, constructor, dtype, function, engine, method):
         self.data.expanding().apply(function, raw=True, engine=engine)
 
+    def time_rolling_methods(self, constructor, dtype, function, engine, method):
+        getattr(self.data.rolling(10), method)(engine=engine)
+
 
 class ExpandingMethods:
 

doc/source/user_guide/window.rst

@@ -321,6 +321,10 @@ Numba will be applied in potentially two routines:
 #. If ``func`` is a standard Python function, the engine will `JIT <https://numba.pydata.org/numba-doc/latest/user/overview.html>`__ the passed function. ``func`` can also be a JITed function in which case the engine will not JIT the function again.
 #. The engine will JIT the for loop where the apply function is applied to each window.
 
+.. versionadded:: 1.3
+
+``mean``, ``median``, ``max``, ``min``, and ``sum`` also support the ``engine`` and ``engine_kwargs`` arguments.
+
 The ``engine_kwargs`` argument is a dictionary of keyword arguments that will be passed into the
 `numba.jit decorator <https://numba.pydata.org/numba-doc/latest/reference/jit-compilation.html#numba.jit>`__.
 These keyword arguments will be applied to *both* the passed function (if a standard Python function)

doc/source/whatsnew/v1.3.0.rst

@@ -52,6 +52,7 @@ Other enhancements
 - Improved integer type mapping from pandas to SQLAlchemy when using :meth:`DataFrame.to_sql` (:issue:`35076`)
 - :func:`to_numeric` now supports downcasting of nullable ``ExtensionDtype`` objects (:issue:`33013`)
 - :func:`pandas.read_excel` can now auto detect .xlsb files (:issue:`35416`)
+- :meth:`.Rolling.sum`, :meth:`.Expanding.sum`, :meth:`.Rolling.mean`, :meth:`.Expanding.mean`, :meth:`.Rolling.median`, :meth:`.Expanding.median`, :meth:`.Rolling.max`, :meth:`.Expanding.max`, :meth:`.Rolling.min`, and :meth:`.Expanding.min` now support ``Numba`` execution with the ``engine`` keyword (:issue:`38895`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/window/expanding.py

@@ -172,33 +172,33 @@ def apply(
 
     @Substitution(name="expanding")
     @Appender(_shared_docs["sum"])
-    def sum(self, *args, **kwargs):
+    def sum(self, *args, engine=None, engine_kwargs=None, **kwargs):
         nv.validate_expanding_func("sum", args, kwargs)
-        return super().sum(*args, **kwargs)
+        return super().sum(*args, engine=engine, engine_kwargs=engine_kwargs, **kwargs)
 
     @Substitution(name="expanding", func_name="max")
     @Appender(_doc_template)
     @Appender(_shared_docs["max"])
-    def max(self, *args, **kwargs):
+    def max(self, *args, engine=None, engine_kwargs=None, **kwargs):
         nv.validate_expanding_func("max", args, kwargs)
-        return super().max(*args, **kwargs)
+        return super().max(*args, engine=engine, engine_kwargs=engine_kwargs, **kwargs)
 
     @Substitution(name="expanding")
     @Appender(_shared_docs["min"])
-    def min(self, *args, **kwargs):
+    def min(self, *args, engine=None, engine_kwargs=None, **kwargs):
         nv.validate_expanding_func("min", args, kwargs)
-        return super().min(*args, **kwargs)
+        return super().min(*args, engine=engine, engine_kwargs=engine_kwargs, **kwargs)
 
     @Substitution(name="expanding")
     @Appender(_shared_docs["mean"])
-    def mean(self, *args, **kwargs):
+    def mean(self, *args, engine=None, engine_kwargs=None, **kwargs):
         nv.validate_expanding_func("mean", args, kwargs)
-        return super().mean(*args, **kwargs)
+        return super().mean(*args, engine=engine, engine_kwargs=engine_kwargs, **kwargs)
 
     @Substitution(name="expanding")
     @Appender(_shared_docs["median"])
-    def median(self, **kwargs):
-        return super().median(**kwargs)
+    def median(self, engine=None, engine_kwargs=None, **kwargs):
+        return super().median(engine=engine, engine_kwargs=engine_kwargs, **kwargs)
 
     @Substitution(name="expanding", versionadded="")
     @Appender(_shared_docs["std"])
@@ -256,9 +256,16 @@ def kurt(self, **kwargs):
 
     @Substitution(name="expanding")
     @Appender(_shared_docs["quantile"])
-    def quantile(self, quantile, interpolation="linear", **kwargs):
+    def quantile(
+        self,
+        quantile,
+        interpolation="linear",
+        **kwargs,
+    ):
         return super().quantile(
-            quantile=quantile, interpolation=interpolation, **kwargs
+            quantile=quantile,
+            interpolation=interpolation,
+            **kwargs,
         )
 
     @Substitution(name="expanding", func_name="cov")