Merge pull request pandas-dev#11450 from kawochen/CLN-PERF-roll-median

CLN/PERF: remove used functions; use C skip list for rolling median

asv_bench/benchmarks/gil.py

@@ -366,3 +366,73 @@ def time_period_to_datetime(self):
         def run(period):
             period.to_timestamp()
         run(self.period)
+
+
+class nogil_rolling_algos_slow(object):
+    goal_time = 0.2
+
+    def setup(self):
+        self.win = 100
+        np.random.seed(1234)
+        self.arr = np.random.rand(100000)
+        if (not have_real_test_parallel):
+            raise NotImplementedError
+
+    def time_nogil_rolling_median(self):
+        @test_parallel(num_threads=2)
+        def run(arr, win):
+            rolling_median(arr, win)
+        run(self.arr, self.win)
+
+
+class nogil_rolling_algos_fast(object):
+    goal_time = 0.2
+
+    def setup(self):
+        self.win = 100
+        np.random.seed(1234)
+        self.arr = np.random.rand(1000000)
+        if (not have_real_test_parallel):
+            raise NotImplementedError
+
+    def time_nogil_rolling_mean(self):
+        @test_parallel(num_threads=2)
+        def run(arr, win):
+            rolling_mean(arr, win)
+        run(self.arr, self.win)
+
+    def time_nogil_rolling_min(self):
+        @test_parallel(num_threads=2)
+        def run(arr, win):
+            rolling_min(arr, win)
+        run(self.arr, self.win)
+
+    def time_nogil_rolling_max(self):
+        @test_parallel(num_threads=2)
+        def run(arr, win):
+            rolling_max(arr, win)
+        run(self.arr, self.win)
+
+    def time_nogil_rolling_var(self):
+        @test_parallel(num_threads=2)
+        def run(arr, win):
+            rolling_var(arr, win)
+        run(self.arr, self.win)
+
+    def time_nogil_rolling_skew(self):
+        @test_parallel(num_threads=2)
+        def run(arr, win):
+            rolling_skew(arr, win)
+        run(self.arr, self.win)
+
+    def time_nogil_rolling_kurt(self):
+        @test_parallel(num_threads=2)
+        def run(arr, win):
+            rolling_kurt(arr, win)
+        run(self.arr, self.win)
+
+    def time_nogil_rolling_std(self):
+        @test_parallel(num_threads=2)
+        def run(arr, win):
+            rolling_std(arr, win)
+        run(self.arr, self.win)

asv_bench/benchmarks/stat_ops.py

@@ -231,6 +231,31 @@ class stats_rolling_mean(object):
 
     def setup(self):
         self.arr = np.random.randn(100000)
+        self.win = 100
 
-    def time_stats_rolling_mean(self):
-        rolling_mean(self.arr, 100)
+    def time_rolling_mean(self):
+        rolling_mean(self.arr, self.win)
+
+    def time_rolling_median(self):
+        rolling_median(self.arr, self.win)
+
+    def time_rolling_min(self):
+        rolling_min(self.arr, self.win)
+
+    def time_rolling_max(self):
+        rolling_max(self.arr, self.win)
+
+    def time_rolling_sum(self):
+        rolling_sum(self.arr, self.win)
+
+    def time_rolling_std(self):
+        rolling_std(self.arr, self.win)
+
+    def time_rolling_var(self):
+        rolling_var(self.arr, self.win)
+
+    def time_rolling_skew(self):
+        rolling_skew(self.arr, self.win)
+
+    def time_rolling_kurt(self):
+        rolling_kurt(self.arr, self.win)

doc/source/whatsnew/v0.17.1.txt

@@ -59,7 +59,8 @@ Performance Improvements
 
 
 - Release the GIL on most datetime field operations (e.g. ``DatetimeIndex.year``, ``Series.dt.year``), normalization, and conversion to and from ``Period``, ``DatetimeIndex.to_period`` and ``PeriodIndex.to_timestamp`` (:issue:`11263`)
-
+- Release the GIL on some srolling algos (``rolling_median``, ``rolling_mean``, ``rolling_max``, ``rolling_min``, ``rolling_var``, ``rolling_kurt``, `rolling_skew`` (:issue:`11450`)
+- Improved performance of ``rolling_median`` (:issue:`11450`)
 
 - Improved performance to ``to_excel`` (:issue:`11352`)