ENH: Add table-wise numba rolling to other agg funcions

ci/deps/azure-37-slow.yaml

@@ -36,3 +36,4 @@ dependencies:
   - xlwt
   - moto
   - flask
+  - numba

ci/deps/azure-38-slow.yaml

@@ -34,3 +34,4 @@ dependencies:
   - xlwt
   - moto
   - flask
+  - numba

doc/source/whatsnew/v1.3.0.rst

@@ -37,7 +37,7 @@ For example:
 
 :class:`Rolling` and :class:`Expanding` now support a ``method`` argument with a
 ``'table'`` option that performs the windowing operation over an entire :class:`DataFrame`.
-See ref:`window.overview` for performance and functional benefits. (:issue:`15095`)
+See ref:`window.overview` for performance and functional benefits. (:issue:`15095`, :issue:`38995`)
 
 .. _whatsnew_130.enhancements.other:
 

pandas/core/window/numba_.py

@@ -1,3 +1,4 @@
+import functools
 from typing import Any, Callable, Dict, Optional, Tuple
 
 import numpy as np
@@ -220,3 +221,21 @@ def roll_table(
         return result
 
     return roll_table
+
+
+# This function will no longer be needed once numba supports
+# axis for all np.nan* agg functions
+# https://github.com/numba/numba/issues/1269
+@functools.lru_cache(maxsize=None)
+def generate_manual_numpy_nan_agg_with_axis(nan_func):
+    numba = import_optional_dependency("numba")
+
+    @numba.jit(nopython=True, nogil=True, parallel=True)
+    def nan_agg_with_axis(table):
+        result = np.empty(table.shape[1])
+        for i in numba.prange(table.shape[1]):
+            partition = table[:, i]
+            result[i] = nan_func(partition)
+        return result
+
+    return nan_agg_with_axis

pandas/core/window/rolling.py

@@ -65,6 +65,7 @@
     VariableWindowIndexer,
 )
 from pandas.core.window.numba_ import (
+    generate_manual_numpy_nan_agg_with_axis,
     generate_numba_apply_func,
     generate_numba_table_func,
 )
@@ -1378,16 +1379,15 @@ def sum(self, *args, engine=None, engine_kwargs=None, **kwargs):
         nv.validate_window_func("sum", args, kwargs)
         if maybe_use_numba(engine):
             if self.method == "table":
-                raise NotImplementedError("method='table' is not supported.")
-            # Once numba supports np.nansum with axis, args will be relevant.
-            # https://github.com/numba/numba/issues/6610
-            args = () if self.method == "single" else (0,)
+                func = generate_manual_numpy_nan_agg_with_axis(np.nansum)
+            else:
+                func = np.nansum
+
             return self.apply(
-                np.nansum,
+                func,
                 raw=True,
                 engine=engine,
                 engine_kwargs=engine_kwargs,
-                args=args,
             )
         window_func = window_aggregations.roll_sum
         return self._apply(window_func, name="sum", **kwargs)
@@ -1424,16 +1424,15 @@ def max(self, *args, engine=None, engine_kwargs=None, **kwargs):
         nv.validate_window_func("max", args, kwargs)
         if maybe_use_numba(engine):
             if self.method == "table":
-                raise NotImplementedError("method='table' is not supported.")
-            # Once numba supports np.nanmax with axis, args will be relevant.
-            # https://github.com/numba/numba/issues/6610
-            args = () if self.method == "single" else (0,)
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmax)
+            else:
+                func = np.nanmax
+
             return self.apply(
-                np.nanmax,
+                func,
                 raw=True,
                 engine=engine,
                 engine_kwargs=engine_kwargs,
-                args=args,
             )
         window_func = window_aggregations.roll_max
         return self._apply(window_func, name="max", **kwargs)
@@ -1496,16 +1495,15 @@ def min(self, *args, engine=None, engine_kwargs=None, **kwargs):
         nv.validate_window_func("min", args, kwargs)
         if maybe_use_numba(engine):
             if self.method == "table":
-                raise NotImplementedError("method='table' is not supported.")
-            # Once numba supports np.nanmin with axis, args will be relevant.
-            # https://github.com/numba/numba/issues/6610
-            args = () if self.method == "single" else (0,)
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmin)
+            else:
+                func = np.nanmin
+
             return self.apply(
-                np.nanmin,
+                func,
                 raw=True,
                 engine=engine,
                 engine_kwargs=engine_kwargs,
-                args=args,
             )
         window_func = window_aggregations.roll_min
         return self._apply(window_func, name="min", **kwargs)
@@ -1514,16 +1512,15 @@ def mean(self, *args, engine=None, engine_kwargs=None, **kwargs):
         nv.validate_window_func("mean", args, kwargs)
         if maybe_use_numba(engine):
             if self.method == "table":
-                raise NotImplementedError("method='table' is not supported.")
-            # Once numba supports np.nanmean with axis, args will be relevant.
-            # https://github.com/numba/numba/issues/6610
-            args = () if self.method == "single" else (0,)
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmean)
+            else:
+                func = np.nanmean
+
             return self.apply(
-                np.nanmean,
+                func,
                 raw=True,
                 engine=engine,
                 engine_kwargs=engine_kwargs,
-                args=args,
             )
         window_func = window_aggregations.roll_mean
         return self._apply(window_func, name="mean", **kwargs)
@@ -1584,16 +1581,15 @@ def mean(self, *args, engine=None, engine_kwargs=None, **kwargs):
     def median(self, engine=None, engine_kwargs=None, **kwargs):
         if maybe_use_numba(engine):
             if self.method == "table":
-                raise NotImplementedError("method='table' is not supported.")
-            # Once numba supports np.nanmedian with axis, args will be relevant.
-            # https://github.com/numba/numba/issues/6610
-            args = () if self.method == "single" else (0,)
+                func = generate_manual_numpy_nan_agg_with_axis(np.nanmedian)
+            else:
+                func = np.nanmedian
+
             return self.apply(
-                np.nanmedian,
+                func,
                 raw=True,
                 engine=engine,
                 engine_kwargs=engine_kwargs,
-                args=args,
             )
         window_func = window_aggregations.roll_median_c
         return self._apply(window_func, name="median", **kwargs)

pandas/tests/window/test_numba.py

@@ -163,6 +163,7 @@ def test_invalid_kwargs_nopython():
 
 
 @td.skip_if_no("numba", "0.46.0")
+@pytest.mark.slow
 @pytest.mark.filterwarnings("ignore:\\nThe keyword argument")
 # Filter warnings when parallel=True and the function can't be parallelized by Numba
 class TestTableMethod:
@@ -177,9 +178,6 @@ def f(x):
                 f, engine="numba", raw=True
             )
 
-    @pytest.mark.xfail(
-        raises=NotImplementedError, reason="method='table' is not supported."
-    )
     def test_table_method_rolling_methods(
         self, axis, nogil, parallel, nopython, arithmetic_numba_supported_operators
     ):
@@ -247,9 +245,6 @@ def f(x):
         )
         tm.assert_frame_equal(result, expected)
 
-    @pytest.mark.xfail(
-        raises=NotImplementedError, reason="method='table' is not supported."
-    )
     def test_table_method_expanding_methods(
         self, axis, nogil, parallel, nopython, arithmetic_numba_supported_operators
     ):