REF: Remove rolling window fixed algorithms #36567

mroeschke · 2020-09-23T06:33:13Z

tests added / passed
passes black pandas
passes git diff upstream/master -u -- "*.py" | flake8 --diff

Pros:

Less code to maintain + makes internals simplier
Fixed a bug: https://github.com/pandas-dev/pandas/pull/36567/files#diff-7656adc204bcddba0534588cabdab62eR770

Cons:

A performance hit

       before           after         ratio
     [d9722efe]       [198ab9e8]
     <clean/rolling_aggregations^2>       <clean/rolling_aggregations>
+     1.27±0.02ms         4.05±1ms     3.19  rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'linear')
+     1.27±0.01ms       3.67±0.7ms     2.88  rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'nearest')
+     1.27±0.01ms       3.22±0.3ms     2.54  rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'higher')
+     1.27±0.01ms      2.99±0.08ms     2.36  rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'midpoint')
+        1.29±0ms      2.92±0.01ms     2.26  rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'nearest')
+        1.29±0ms      2.91±0.01ms     2.26  rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'midpoint')
+     1.29±0.08ms      2.91±0.01ms     2.25  rolling.Methods.time_rolling('Series', 10, 'float', 'max')
+     1.29±0.01ms         2.91±0ms     2.25  rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'higher')
+     1.29±0.01ms      2.90±0.02ms     2.24  rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'linear')
+     1.30±0.02ms      2.91±0.04ms     2.24  rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'lower')
+     1.30±0.03ms      2.90±0.01ms     2.23  rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'lower')
+     1.32±0.08ms      2.93±0.02ms     2.22  rolling.Methods.time_rolling('Series', 10, 'float', 'min')
+     1.33±0.03ms         2.95±0ms     2.21  rolling.Methods.time_rolling('Series', 10, 'int', 'max')
+     1.44±0.01ms      3.11±0.03ms     2.16  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'lower')
+     1.37±0.05ms      2.96±0.01ms     2.16  rolling.Methods.time_rolling('Series', 10, 'int', 'min')
+     1.44±0.01ms      3.10±0.02ms     2.15  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'midpoint')
+     1.44±0.01ms      3.07±0.01ms     2.14  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'higher')
+        1.44±0ms      3.08±0.01ms     2.14  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'nearest')
+     1.45±0.01ms      3.07±0.01ms     2.13  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'max')
+     1.46±0.01ms      3.07±0.01ms     2.10  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'linear')
+        1.47±0ms      3.09±0.02ms     2.10  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'linear')
+     1.48±0.02ms      3.10±0.02ms     2.10  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'higher')
+        1.47±0ms         3.08±0ms     2.09  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'min')
+     1.47±0.01ms      3.07±0.01ms     2.09  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'midpoint')
+      1.48±0.2ms      3.10±0.02ms     2.09  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'nearest')
+     1.47±0.04ms      3.05±0.02ms     2.08  rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'linear')
+     1.52±0.01ms      3.15±0.02ms     2.07  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'max')
+     1.54±0.01ms      3.17±0.03ms     2.06  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'min')
+        1.49±0ms      3.05±0.06ms     2.04  rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'nearest')
+        1.49±0ms      3.03±0.03ms     2.03  rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'lower')
+        1.49±0ms      2.98±0.03ms     1.99  rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'midpoint')
+     1.40±0.01ms      2.75±0.01ms     1.97  rolling.ExpandingMethods.time_expanding('Series', 'float', 'max')
+        1.49±0ms      2.93±0.02ms     1.96  rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'higher')
+     1.44±0.01ms      2.81±0.02ms     1.96  rolling.ExpandingMethods.time_expanding('Series', 'int', 'max')
+      1.59±0.2ms      3.11±0.03ms     1.95  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'lower')
+         930±3μs      1.80±0.01ms     1.94  rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'higher')
+        1.52±0ms      2.94±0.02ms     1.93  rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'linear')
+      1.51±0.2ms      2.92±0.01ms     1.93  rolling.Methods.time_rolling('Series', 1000, 'float', 'max')
+         933±4μs      1.80±0.01ms     1.93  rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'nearest')
+         931±4μs      1.79±0.01ms     1.93  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'nearest')
+     1.69±0.01ms      3.26±0.01ms     1.93  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'linear')
+         933±4μs      1.80±0.01ms     1.93  rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'midpoint')
+      1.52±0.2ms      2.93±0.01ms     1.93  rolling.Methods.time_rolling('Series', 1000, 'float', 'min')
+        937±10μs      1.80±0.01ms     1.92  rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'lower')
+        1.56±0ms      2.99±0.05ms     1.92  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'max')
+        940±30μs      1.80±0.01ms     1.91  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'midpoint')
+     1.56±0.02ms      2.98±0.01ms     1.91  rolling.Methods.time_rolling('Series', 1000, 'int', 'max')
+         935±3μs      1.79±0.01ms     1.91  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'higher')
+         934±7μs      1.78±0.01ms     1.91  rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'linear')
+         931±3μs      1.78±0.01ms     1.91  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'lower')
+     1.45±0.01ms         2.76±0ms     1.91  rolling.ExpandingMethods.time_expanding('Series', 'float', 'min')
+        1.63±0ms      3.11±0.01ms     1.90  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'nearest')
+         937±4μs      1.78±0.01ms     1.90  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'linear')
+         961±2μs      1.82±0.04ms     1.90  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'midpoint')
+     1.49±0.02ms      2.82±0.01ms     1.89  rolling.ExpandingMethods.time_expanding('Series', 'int', 'min')
+         964±9μs      1.82±0.02ms     1.88  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'nearest')
+         963±7μs      1.81±0.05ms     1.88  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'linear')
+         962±8μs      1.81±0.01ms     1.88  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'higher')
+         967±3μs      1.81±0.01ms     1.88  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'lower')
+         966±3μs      1.81±0.02ms     1.87  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'linear')
+         964±1μs      1.80±0.02ms     1.87  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'nearest')
+     1.66±0.03ms      3.10±0.01ms     1.87  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'nearest')
+         964±4μs         1.80±0ms     1.87  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'higher')
+        1.12±0ms      2.08±0.01ms     1.87  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest')
+     1.67±0.01ms      3.10±0.01ms     1.86  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'midpoint')
+     1.63±0.02ms      3.02±0.03ms     1.86  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'max')
+     1.68±0.01ms      3.11±0.01ms     1.86  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'lower')
+         969±3μs      1.80±0.02ms     1.85  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'midpoint')
+     1.68±0.01ms      3.11±0.02ms     1.85  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'higher')
+     1.61±0.09ms      2.97±0.01ms     1.85  rolling.Methods.time_rolling('Series', 1000, 'int', 'min')
+         970±7μs      1.79±0.01ms     1.84  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'lower')
+     1.72±0.01ms       3.16±0.1ms     1.84  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'linear')
+     1.71±0.02ms      3.16±0.07ms     1.84  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'max')
+     1.70±0.08ms      3.13±0.02ms     1.84  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'min')
+     1.70±0.07ms      3.11±0.01ms     1.83  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'lower')
+     1.71±0.01ms      3.13±0.02ms     1.83  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'higher')
+     1.65±0.01ms      3.00±0.02ms     1.82  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'min')
+     1.15±0.01ms      2.09±0.01ms     1.82  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'linear')
+     1.71±0.02ms      3.11±0.01ms     1.82  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'max')
+        1.71±0ms      3.11±0.01ms     1.82  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'midpoint')
+     1.62±0.01ms      2.94±0.03ms     1.82  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'min')
+        1.12±0ms      2.02±0.05ms     1.80  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher')
+     1.76±0.08ms      3.15±0.01ms     1.80  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'min')
+     1.12±0.01ms      1.99±0.03ms     1.78  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'nearest')
+     1.12±0.01ms      1.99±0.02ms     1.78  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'lower')
+        1.14±0ms      2.03±0.03ms     1.78  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'higher')
+        1.12±0ms      1.99±0.01ms     1.78  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'higher')
+        1.15±0ms      2.03±0.05ms     1.77  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear')
+     1.13±0.01ms      2.00±0.05ms     1.77  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest')
+        1.12±0ms      1.98±0.04ms     1.77  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'lower')
+        1.12±0ms      1.97±0.01ms     1.76  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'midpoint')
+     1.13±0.01ms      1.99±0.01ms     1.76  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'midpoint')
+        1.12±0ms      1.97±0.01ms     1.76  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'linear')
+     1.14±0.01ms      1.98±0.01ms     1.74  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'midpoint')
+     1.14±0.02ms         1.97±0ms     1.74  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'lower')
+     1.14±0.01ms         1.97±0ms     1.73  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'higher')
+     1.14±0.01ms      1.97±0.01ms     1.73  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'midpoint')
+        1.14±0ms      1.97±0.01ms     1.72  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'nearest')
+     1.14±0.01ms      1.97±0.01ms     1.72  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'lower')
+     1.46±0.07ms      2.48±0.01ms     1.70  rolling.Methods.time_rolling('Series', 10, 'float', 'std')
+     1.21±0.09ms      2.00±0.02ms     1.66  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'linear')
+     1.54±0.03ms      2.54±0.01ms     1.65  rolling.Methods.time_rolling('Series', 1000, 'int', 'std')
+     1.62±0.04ms      2.66±0.01ms     1.65  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'std')
+     1.68±0.01ms      2.75±0.02ms     1.64  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'std')
+      1.56±0.1ms      2.53±0.01ms     1.63  rolling.Methods.time_rolling('Series', 10, 'int', 'std')
+     1.66±0.03ms      2.67±0.01ms     1.61  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'std')
+     1.71±0.03ms      2.73±0.01ms     1.59  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'std')
+        913±40μs         1.42±0ms     1.56  rolling.Methods.time_rolling('Series', 10, 'float', 'mean')
+      1.62±0.2ms      2.49±0.01ms     1.54  rolling.Methods.time_rolling('Series', 1000, 'float', 'std')
+       933±100μs      1.43±0.01ms     1.53  rolling.Methods.time_rolling('Series', 1000, 'float', 'mean')
+        980±30μs      1.48±0.01ms     1.51  rolling.Methods.time_rolling('Series', 10, 'int', 'mean')
+     1.07±0.01ms      1.60±0.01ms     1.50  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'mean')
+        985±60μs      1.47±0.01ms     1.50  rolling.Methods.time_rolling('Series', 1000, 'int', 'mean')
+        1.21±0ms      1.81±0.01ms     1.49  rolling.ExpandingMethods.time_expanding('Series', 'float', 'kurt')
+        1.43±0ms      2.11±0.01ms     1.47  rolling.Methods.time_rolling('Series', 1000, 'int', 'kurt')
+        1.14±0ms      1.68±0.01ms     1.47  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'mean')
+        1.26±0ms         1.86±0ms     1.47  rolling.ExpandingMethods.time_expanding('Series', 'int', 'kurt')
+     1.40±0.08ms      2.06±0.01ms     1.47  rolling.Methods.time_rolling('Series', 10, 'float', 'kurt')
+     1.14±0.01ms         1.67±0ms     1.46  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'mean')
+     1.12±0.01ms      1.63±0.01ms     1.45  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'mean')
+     1.55±0.01ms      2.24±0.01ms     1.44  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'kurt')
+         846±4μs         1.22±0ms     1.44  rolling.ExpandingMethods.time_expanding('Series', 'float', 'mean')
+     1.38±0.01ms      1.98±0.01ms     1.43  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'kurt')
+        1.62±0ms      2.30±0.01ms     1.42  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'kurt')
+     1.27±0.02ms      1.80±0.02ms     1.41  rolling.Methods.time_rolling('Series', 10, 'float', 'skew')
+     1.57±0.02ms      2.22±0.06ms     1.41  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+     1.32±0.08ms      1.86±0.01ms     1.41  rolling.Methods.time_rolling('Series', 10, 'int', 'skew')
+     1.44±0.01ms      2.03±0.01ms     1.41  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'kurt')
+         895±3μs         1.26±0ms     1.40  rolling.ExpandingMethods.time_expanding('Series', 'int', 'mean')
+     1.32±0.06ms         1.84±0ms     1.40  rolling.Methods.time_rolling('Series', 1000, 'int', 'skew')
+     1.64±0.04ms      2.29±0.01ms     1.40  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'kurt')
+     1.42±0.01ms         1.97±0ms     1.39  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'skew')
+        900±50μs      1.25±0.02ms     1.38  rolling.Methods.time_rolling('Series', 10, 'float', 'sum')
+     2.12±0.07ms      2.92±0.01ms     1.38  rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'lower')
+     1.49±0.01ms         2.04±0ms     1.37  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'skew')
+     1.45±0.04ms      1.98±0.03ms     1.37  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'skew')
+     1.51±0.02ms      2.05±0.01ms     1.36  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'skew')
+      1.51±0.2ms      2.05±0.01ms     1.36  rolling.Methods.time_rolling('Series', 1000, 'float', 'kurt')
+     1.02±0.01ms      1.38±0.01ms     1.36  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'mean')
+     1.08±0.01ms      1.46±0.02ms     1.36  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'mean')
+        962±60μs         1.28±0ms     1.34  rolling.Methods.time_rolling('Series', 10, 'int', 'sum')
+         841±5μs      1.12±0.01ms     1.33  rolling.ExpandingMethods.time_expanding('Series', 'float', 'sum')
+     1.26±0.01ms      1.66±0.02ms     1.33  rolling.ExpandingMethods.time_expanding('Series', 'int', 'skew')
+        969±80μs      1.28±0.01ms     1.32  rolling.Methods.time_rolling('Series', 1000, 'int', 'sum')
+     1.20±0.02ms      1.58±0.01ms     1.31  rolling.ExpandingMethods.time_expanding('Series', 'float', 'skew')
+     1.14±0.01ms      1.48±0.01ms     1.30  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'sum')
+         899±5μs         1.17±0ms     1.30  rolling.ExpandingMethods.time_expanding('Series', 'int', 'sum')
+     1.08±0.04ms      1.40±0.01ms     1.30  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'sum')
+     1.09±0.04ms      1.41±0.01ms     1.29  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'sum')
+        1.36±0ms      1.75±0.01ms     1.29  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'skew')
+     1.15±0.06ms      1.47±0.01ms     1.28  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'sum')
+        1.01±0ms         1.28±0ms     1.27  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'sum')
+        1.43±0ms      1.81±0.01ms     1.27  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'skew')
+     1.22±0.03ms      1.54±0.01ms     1.26  rolling.Methods.time_rolling('Series', 10, 'float', 'count')
+        1.10±0ms      1.38±0.01ms     1.26  rolling.ExpandingMethods.time_expanding('Series', 'int', 'count')
+     1.07±0.01ms         1.35±0ms     1.26  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'sum')
+        1.36±0ms      1.71±0.02ms     1.26  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'count')
+     1.40±0.01ms      1.74±0.01ms     1.24  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'count')
+     1.14±0.01ms         1.40±0ms     1.23  rolling.ExpandingMethods.time_expanding('Series', 'float', 'count')
+      1.22±0.1ms         1.49±0ms     1.22  rolling.Methods.time_rolling('Series', 10, 'int', 'count')
+     1.40±0.04ms      1.70±0.01ms     1.21  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'count')
+     1.43±0.09ms      1.73±0.01ms     1.21  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'count')
+        1.29±0ms      1.56±0.01ms     1.21  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'count')
+     1.33±0.01ms      1.60±0.01ms     1.20  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'count')
+        1.38±0ms      1.66±0.01ms     1.20  rolling.ExpandingMethods.time_expanding('Series', 'float', 'std')
+     1.44±0.01ms      1.72±0.01ms     1.19  rolling.ExpandingMethods.time_expanding('Series', 'int', 'std')
+     1.56±0.01ms         1.84±0ms     1.18  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'std')
+     1.62±0.01ms      1.89±0.01ms     1.17  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'std')

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.

… with center tests still failing

…regations

pandas/_libs/window/aggregations.pyx

…regations

mroeschke · 2020-09-25T04:17:58Z

pandas/tests/window/test_rolling.py

@@ -767,7 +767,7 @@ def test_rolling_numerical_too_large_numbers():
    ds[2] = -9e33
    result = ds.rolling(5).mean()
    expected = pd.Series(
-        [np.nan, np.nan, np.nan, np.nan, -1.8e33, -1.8e33, -1.8e33, 0.0, 6.0, 7.0],
+        [np.nan, np.nan, np.nan, np.nan, -1.8e33, -1.8e33, -1.8e33, 5.0, 6.0, 7.0],


It was thought that the 0 was expected due to numerical precision, but by using the variable algorithm instead of the fixed algorithm we get the correct value

xref #11645 (comment)

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jreback · 2020-09-26T01:41:01Z

so all of your bencharmarks on quantile are a bit misleading; they are actually measuring the perf diff in min/max (which is what percentile 0 or 1 does)

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jreback · 2020-09-27T19:34:49Z

pandas/_libs/window/aggregations.pyx

@@ -414,7 +356,7 @@ cdef inline float64_t calc_var(int64_t minp, int ddof, float64_t nobs,
            result = 0
        else:
            result = ssqdm_x / (nobs - <float64_t>ddof)
-            if result < 0:
+            if result < 1e-15:


is this new? ok but can you add a comment

Sure, turns out var/std doesn't use Kahan Summation so using the variable algorithm has a small numerical imprecision

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jreback · 2020-10-02T20:11:43Z

let's file an issue for followon to see if we can improve min//max rolling with variable.

jreback · 2020-10-02T20:11:55Z

any comments @TomAugspurger

jreback

if you can add a release, e.g. slight performance decrease in min/max fixed algos.

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jreback · 2020-10-02T20:31:28Z

do these changes help / hurt #36132 ? (maybe worth pulling in and xfailing these tests)? though that can certainly be a followon

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mroeschke · 2020-10-02T21:07:11Z

Yeah I was thinking #36132 could be simplified once this PR is merged in. Probably best on a follow-on

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mroeschke · 2020-10-05T04:50:34Z

All green

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jreback · 2020-10-05T21:33:22Z

cc @pandas-dev/pandas-core if any comments.

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jreback · 2020-10-09T20:04:43Z

can you merge master and ping on green.

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mroeschke · 2020-10-09T22:13:54Z

@jreback green

jreback · 2020-10-09T23:16:50Z

thanks @mroeschke very nice

* Fix rolling test result, removed fixed algorithms, some rolling apply with center tests still failing * Rename function and move offset to correct location * Impliment center in terms of indexers * Get all the tests to pass * Remove center from _apply as no longer needed * Add better typing * Deal with numeric precision * Remove code related to center now being implimented in the fixed indexer * Add note regarding precision issues * Note performance hit * Remove tilde * Change useage of get_cython_func_type * Remove self.center from count's _apply Co-authored-by: Matt Roeschke <[email protected]>

Matt Roeschke added 4 commits September 20, 2020 10:32

Fix rolling test result, removed fixed algorithms, some rolling apply…

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… with center tests still failing

Merge remote-tracking branch 'upstream/master' into clean/rolling_agg…

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Rename function and move offset to correct location

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Merge remote-tracking branch 'upstream/master' into clean/rolling_agg…

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jbrockmendel reviewed Sep 23, 2020

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Matt Roeschke added 3 commits September 24, 2020 00:20

Merge remote-tracking branch 'upstream/master' into clean/rolling_agg…

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Impliment center in terms of indexers

f057ac6

Get all the tests to pass

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mroeschke mentioned this pull request Sep 25, 2020

API: reimplement FixedWindowIndexer.get_window_bounds to fix groupby bug #36132

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Remove center from _apply as no longer needed

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mroeschke changed the title ~~WIP: REF: Remove rolling window fixed algorithms~~ REF: Remove rolling window fixed algorithms Sep 25, 2020

Add better typing

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mroeschke commented Sep 25, 2020

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mroeschke added Refactor Internal refactoring of code Window rolling, ewma, expanding labels Sep 25, 2020

mroeschke added this to the 1.2 milestone Sep 25, 2020

Deal with numeric precision

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Matt Roeschke added 2 commits September 27, 2020 12:22

Remove code related to center now being implimented in the fixed indexer

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Merge remote-tracking branch 'upstream/master' into clean/rolling_agg…

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jreback reviewed Sep 27, 2020

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Matt Roeschke added 4 commits September 27, 2020 12:44

Add note regarding precision issues

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jreback requested changes Oct 2, 2020

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Merge remote-tracking branch 'upstream/master' into clean/rolling_agg…

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Matt Roeschke added 2 commits October 2, 2020 13:25

Note performance hit

e6cf772

Remove tilde

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Merge remote-tracking branch 'upstream/master' into clean/rolling_agg…

8b0fa34

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Matt Roeschke added 3 commits October 2, 2020 14:27

Change useage of get_cython_func_type

4913c84

Merge remote-tracking branch 'upstream/master' into clean/rolling_agg…

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mroeschke mentioned this pull request Oct 4, 2020

TRACKER: milestones twosigma/pandas#44

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Matt Roeschke added 2 commits October 4, 2020 16:10

Merge remote-tracking branch 'upstream/master' into clean/rolling_agg…

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Remove self.center from count's _apply

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Matt Roeschke added 4 commits October 6, 2020 18:38

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jreback approved these changes Oct 9, 2020

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jreback merged commit 846cff9 into pandas-dev:master Oct 9, 2020

mroeschke deleted the clean/rolling_aggregations branch October 9, 2020 23:34

mroeschke mentioned this pull request Oct 10, 2020

PERF: Investivate potential performance improvements for rolling min/max #37022

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This was referenced Oct 10, 2020

API: reimplement FixedWindowIndexer.get_window_bounds #37035

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BUG: Rolling min_periods not working on groupby object #36040

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simonjayhawkins mentioned this pull request Jan 1, 2021

REGR: incorrect results with std on rolling window since 1.2.0 #38874

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REF: Remove rolling window fixed algorithms #36567

REF: Remove rolling window fixed algorithms #36567

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REF: Remove rolling window fixed algorithms #36567

REF: Remove rolling window fixed algorithms #36567

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