ENH: Improve numerical stability for window functions skew and kurt
- [x] closes #6929
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

Used the same procedure as in ``DataFrame.skew()``
Edit: Have to run asvs them again because of code changes Hello @phofl! Thanks for updating this PR. We checked the lines you've touched for [PEP 8](https://www.python.org/dev/peps/pep-0008) issues, and found:





There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: 

##### Comment last updated at 2020-11-09 11:16:48 UTC Got somewhat ugly now through the Kahan summation for all parts. 
Can not subtract mean for wide range of values, because this would lead to imprecision for the small values. Running asvs now again ```
       before           after         ratio
     [6092dc7d]       [c58be86a]
     <6929~3^2>       <6929>    
+     1.96±0.03ms       3.11±0.3ms     1.59  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'skew')
+     1.92±0.06ms       3.03±0.3ms     1.58  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'skew')
+     1.92±0.02ms       2.98±0.2ms     1.55  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'skew')
+     1.84±0.05ms      2.79±0.06ms     1.52  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'kurt')
+     2.33±0.07ms       3.49±0.2ms     1.50  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'skew')
+     2.37±0.06ms       3.55±0.3ms     1.50  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'skew')
+     1.86±0.03ms       2.76±0.1ms     1.48  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'kurt')
+     1.84±0.06ms      2.69±0.04ms     1.46  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'skew')
+     1.82±0.04ms      2.65±0.04ms     1.46  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'skew')
+     2.32±0.06ms       3.35±0.3ms     1.45  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'skew')
+     1.36±0.02ms      1.96±0.08ms     1.44  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'kurt')
+     1.84±0.02ms      2.62±0.05ms     1.42  rolling.Methods.time_rolling('Series', 10, 'int', 'skew')
+     2.15±0.01ms      3.05±0.05ms     1.42  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'skew')
+     2.13±0.01ms       3.02±0.1ms     1.42  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+     1.41±0.02ms      1.99±0.07ms     1.42  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'kurt')
+     2.35±0.02ms      3.34±0.04ms     1.42  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'kurt')
+     1.89±0.02ms      2.66±0.09ms     1.41  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'skew')
+     1.53±0.04ms      2.15±0.05ms     1.40  rolling.ExpandingMethods.time_expanding('Series', 'int', 'skew')
+     1.67±0.04ms      2.33±0.05ms     1.40  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'kurt')
+     2.00±0.01ms      2.80±0.02ms     1.40  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'kurt')
+     1.41±0.03ms      1.96±0.09ms     1.39  rolling.ExpandingMethods.time_expanding('Series', 'float', 'skew')
+     2.02±0.07ms      2.80±0.04ms     1.38  rolling.Methods.time_rolling('Series', 10, 'int', 'kurt')
+     1.81±0.01ms      2.49±0.02ms     1.37  rolling.Methods.time_rolling('Series', 10, 'float', 'skew')
+     2.11±0.06ms      2.90±0.05ms     1.37  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'skew')
+     2.10±0.06ms      2.87±0.05ms     1.37  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'skew')
+     2.29±0.04ms      3.12±0.03ms     1.36  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'skew')
+     2.28±0.05ms      3.10±0.01ms     1.36  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'kurt')
+     2.06±0.05ms      2.81±0.03ms     1.36  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'kurt')
+     2.12±0.01ms      2.88±0.04ms     1.36  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'kurt')
+     2.42±0.02ms      3.29±0.02ms     1.36  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'kurt')
+     2.07±0.05ms      2.78±0.02ms     1.34  rolling.Methods.time_rolling('Series', 1000, 'int', 'kurt')
+     2.27±0.02ms      3.04±0.02ms     1.34  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'skew')
+     1.57±0.01ms      2.11±0.03ms     1.34  rolling.ExpandingMethods.time_expanding('Series', 'int', 'kurt')
+     2.03±0.02ms      2.71±0.05ms     1.34  rolling.Methods.time_rolling('Series', 10, 'float', 'kurt')
+     2.39±0.06ms      3.18±0.07ms     1.34  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'kurt')
+     2.13±0.02ms       2.84±0.1ms     1.33  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'kurt')
+     2.16±0.03ms      2.87±0.05ms     1.33  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'skew')
+     1.74±0.02ms      2.30±0.01ms     1.33  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'skew')
+     1.92±0.03ms      2.54±0.02ms     1.33  rolling.Methods.time_rolling('Series', 1000, 'int', 'skew')
+     2.27±0.03ms      2.99±0.09ms     1.32  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'kurt')
+     2.23±0.03ms      2.94±0.06ms     1.32  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'kurt')
+     2.33±0.01ms      3.08±0.06ms     1.32  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+     1.88±0.03ms      2.47±0.03ms     1.31  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'skew')
+     2.13±0.06ms      2.79±0.04ms     1.31  rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'kurt')
+     1.74±0.04ms      2.27±0.09ms     1.30  rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'kurt')
+     2.30±0.02ms      2.98±0.02ms     1.30  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'skew')
+     2.45±0.02ms      3.15±0.04ms     1.28  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'kurt')
+     1.94±0.02ms      2.47±0.03ms     1.28  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'kurt')
+     1.54±0.01ms       1.91±0.2ms     1.24  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'lower')
+     4.03±0.03ms       5.00±0.4ms     1.24  rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'std')
+     1.49±0.01ms       1.84±0.2ms     1.23  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'higher')
+     1.53±0.01ms       1.82±0.2ms     1.19  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'nearest')
+     1.52±0.01ms       1.81±0.1ms     1.19  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'midpoint')
+     1.50±0.04ms      1.74±0.02ms     1.16  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'midpoint')
+     1.44±0.01ms      1.64±0.02ms     1.14  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'lower')
+     1.61±0.03ms      1.83±0.04ms     1.14  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'max')
+     2.93±0.01ms      3.33±0.04ms     1.14  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'higher')
+     1.82±0.02ms      2.06±0.01ms     1.13  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest')
+     1.78±0.02ms      2.02±0.01ms     1.13  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest')
+     1.45±0.04ms      1.64±0.02ms     1.13  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'linear')
+     1.60±0.03ms      1.81±0.01ms     1.13  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'min')
+     2.91±0.02ms       3.29±0.2ms     1.13  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'linear')
+     1.31±0.02ms      1.47±0.06ms     1.12  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'min')
+     1.82±0.01ms      2.04±0.02ms     1.12  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear')
+     1.46±0.02ms      1.64±0.02ms     1.12  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'nearest')
+     1.55±0.03ms      1.72±0.02ms     1.11  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'nearest')
+     1.52±0.02ms      1.69±0.01ms     1.11  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'higher')
+     1.52±0.04ms      1.69±0.03ms     1.11  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'linear')
+     1.84±0.02ms      2.04±0.02ms     1.11  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'lower')
+     1.83±0.02ms      2.03±0.02ms     1.11  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher')
+     1.51±0.03ms      1.67±0.03ms     1.11  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'higher')
+     2.67±0.03ms      2.95±0.06ms     1.11  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'max')
+     2.92±0.01ms      3.23±0.06ms     1.11  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'midpoint')
-     1.78±0.03ms      1.61±0.03ms     0.91  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'mean')
-     1.66±0.01ms      1.51±0.02ms     0.91  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'mean')
-       984±100μs          887±6μs     0.90  rolling.EWMMethods.time_ewm_times('Series', 10, 'float', 'mean')
-     1.63±0.02ms      1.44±0.02ms     0.88  rolling.Methods.time_rolling('Series', 10, 'int', 'mean')
-     1.56±0.02ms      1.36±0.01ms     0.87  rolling.Methods.time_rolling('Series', 1000, 'float', 'mean')
-     1.60±0.05ms      1.37±0.03ms     0.86  rolling.Methods.time_rolling('Series', 10, 'float', 'mean')
-      1.77±0.1ms      1.49±0.02ms     0.84  rolling.Methods.time_rolling('Series', 1000, 'int', 'mean')
-      1.04±0.1ms          871±3μs     0.84  rolling.EWMMethods.time_ewm_times('Series', 10, 'float', 'std')
-      1.18±0.1ms         956±10μs     0.81  rolling.EWMMethods.time_ewm_times('Series', 10, 'int', 'std')

```

Would have expected a bigger hit. looks good, can you merge master and ping on green (also maybe want to re-run the perf tests to give a good read) asvs:

```
       before           after         ratio
     [82cd86c1]       [50ea50a7]
     <25165^2>        <6929>    
+      2.39±0.1ms         4.20±1ms     1.76  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'kurt')
+     2.16±0.09ms       3.76±0.8ms     1.74  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'skew')
+      2.22±0.1ms       3.77±0.9ms     1.70  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'skew')
+     2.36±0.09ms       4.00±0.6ms     1.69  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'kurt')
+     2.22±0.04ms       3.76±0.9ms     1.69  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'skew')
+     1.23±0.02ms      2.06±0.03ms     1.67  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'kurt')
+     2.41±0.05ms       4.03±0.7ms     1.67  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'kurt')
+      1.37±0.2ms       2.29±0.2ms     1.67  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'kurt')
+     2.23±0.07ms       3.66±0.6ms     1.64  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'kurt')
+     2.39±0.02ms       3.88±0.6ms     1.62  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'kurt')
+     2.20±0.01ms       3.56±0.2ms     1.62  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'kurt')
+     1.91±0.02ms      3.04±0.04ms     1.59  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'kurt')
+     1.75±0.03ms      2.77±0.01ms     1.58  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'kurt')
+     1.86±0.03ms      2.94±0.03ms     1.58  rolling.Methods.time_rolling('Series', 1000, 'float', 'kurt')
+     1.55±0.04ms      2.43±0.03ms     1.57  rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'kurt')
+     2.18±0.02ms       3.42±0.5ms     1.57  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'skew')
+     2.00±0.02ms      3.12±0.03ms     1.56  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'kurt')
+     1.88±0.02ms      2.92±0.03ms     1.56  rolling.Methods.time_rolling('Series', 10, 'float', 'kurt')
+     1.54±0.02ms      2.40±0.02ms     1.56  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'kurt')
+     1.96±0.01ms      3.04±0.08ms     1.55  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'kurt')
+     2.11±0.03ms      3.25±0.04ms     1.54  rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'kurt')
+      1.82±0.2ms      2.79±0.03ms     1.53  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'kurt')
+        2.00±0ms      3.06±0.06ms     1.53  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'kurt')
+     1.42±0.03ms      2.16±0.02ms     1.52  rolling.ExpandingMethods.time_expanding('Series', 'int', 'kurt')
+     1.91±0.02ms      2.90±0.02ms     1.52  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'kurt')
+     1.99±0.01ms      3.01±0.02ms     1.52  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'kurt')
+     2.07±0.03ms      3.12±0.04ms     1.51  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+     1.71±0.02ms      2.57±0.09ms     1.50  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'skew')
+     1.98±0.03ms      2.97±0.03ms     1.50  rolling.Methods.time_rolling('Series', 1000, 'int', 'kurt')
+     2.27±0.03ms      3.38±0.03ms     1.49  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'kurt')
+     1.65±0.02ms      2.45±0.02ms     1.48  rolling.Methods.time_rolling('Series', 1000, 'float', 'skew')
+     1.71±0.02ms      2.51±0.02ms     1.47  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'skew')
+     2.29±0.03ms      3.36±0.06ms     1.47  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'kurt')
+     1.40±0.02ms      2.05±0.03ms     1.47  rolling.ExpandingMethods.time_expanding('Series', 'float', 'kurt')
+     2.42±0.03ms      3.53±0.05ms     1.46  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'kurt')
+     2.19±0.01ms      3.19±0.04ms     1.46  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+        1.80±0ms      2.60±0.07ms     1.44  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'kurt')
+     1.71±0.03ms      2.47±0.04ms     1.44  rolling.Methods.time_rolling('Series', 10, 'int', 'skew')
+     1.97±0.02ms      2.84±0.04ms     1.44  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'skew')
+     1.73±0.02ms      2.48±0.02ms     1.43  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'skew')
+     1.81±0.01ms      2.59±0.03ms     1.43  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'skew')
+     1.65±0.03ms      2.35±0.03ms     1.43  rolling.Methods.time_rolling('Series', 10, 'float', 'skew')
+     1.81±0.03ms      2.56±0.05ms     1.42  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'skew')
+     2.56±0.02ms         3.63±1ms     1.41  rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'lower')
+     2.04±0.01ms      2.88±0.02ms     1.41  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'skew')
+     1.72±0.01ms      2.42±0.04ms     1.41  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'kurt')
+     2.11±0.03ms      2.96±0.05ms     1.40  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'skew')
+     1.75±0.01ms      2.45±0.05ms     1.40  rolling.Methods.time_rolling('Series', 1000, 'int', 'skew')
+     1.35±0.01ms      1.87±0.03ms     1.39  rolling.ExpandingMethods.time_expanding('Series', 'int', 'skew')
+     1.84±0.03ms      2.53±0.03ms     1.38  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'skew')
+     2.61±0.03ms       3.58±0.9ms     1.37  rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'higher')
+     2.00±0.03ms      2.73±0.06ms     1.37  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'skew')
+     2.04±0.06ms       2.76±0.1ms     1.35  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'skew')
+     2.24±0.02ms      3.02±0.05ms     1.35  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'skew')
+     1.32±0.02ms      1.77±0.01ms     1.35  rolling.ExpandingMethods.time_expanding('Series', 'float', 'skew')
+     1.61±0.01ms      2.17±0.02ms     1.35  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'skew')
+     1.52±0.02ms       2.05±0.3ms     1.35  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'higher')
+     1.74±0.02ms      2.29±0.04ms     1.32  rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'skew')
+     3.13±0.05ms       4.06±0.8ms     1.30  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'max')
+     2.62±0.04ms       3.27±0.3ms     1.25  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'max')
+     1.53±0.02ms      1.90±0.08ms     1.24  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'midpoint')
+     1.53±0.02ms      1.84±0.06ms     1.21  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'higher')
+     1.54±0.02ms      1.81±0.03ms     1.18  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'linear')
+     3.06±0.03ms       3.58±0.4ms     1.17  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'max')
+     1.48±0.02ms      1.72±0.01ms     1.16  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'lower')
+     1.33±0.01ms      1.54±0.01ms     1.16  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'max')
+     1.48±0.02ms         1.71±0ms     1.16  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'higher')
+     2.48±0.03ms      2.87±0.07ms     1.16  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'max')
+     1.84±0.02ms      2.12±0.02ms     1.15  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'midpoint')
+     1.55±0.02ms      1.78±0.02ms     1.15  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'lower')
+     1.50±0.02ms      1.72±0.04ms     1.14  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'linear')
+     1.85±0.02ms      2.10±0.04ms     1.14  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest')
+     1.50±0.03ms      1.71±0.04ms     1.14  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'nearest')
+     1.86±0.05ms      2.11±0.02ms     1.14  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher')
+     1.55±0.02ms      1.76±0.01ms     1.13  rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'nearest')
+     1.52±0.02ms      1.73±0.01ms     1.13  rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'midpoint')
+     1.56±0.02ms      1.76±0.02ms     1.13  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'nearest')
+     1.34±0.02ms      1.52±0.02ms     1.13  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'min')
+     1.83±0.02ms      2.06±0.04ms     1.13  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest')
+     1.83±0.01ms      2.07±0.02ms     1.13  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear')
+     1.85±0.03ms      2.08±0.05ms     1.13  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'lower')
+     1.86±0.02ms      2.10±0.02ms     1.13  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'nearest')
+     1.90±0.02ms      2.14±0.07ms     1.13  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'midpoint')
+     1.83±0.01ms      2.06±0.04ms     1.13  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'linear')
+     2.67±0.02ms       2.98±0.1ms     1.12  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'min')
+     1.87±0.06ms      2.09±0.03ms     1.12  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'linear')
+     1.87±0.01ms      2.07±0.02ms     1.11  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'linear')
+     2.12±0.02ms      2.36±0.01ms     1.11  rolling.ExpandingMethods.time_expanding('Series', 'float', 'max')
+     2.43±0.07ms      2.70±0.01ms     1.11  rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'max')
+     1.74±0.02ms      1.93±0.01ms     1.11  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'max')
+     1.83±0.02ms      2.03±0.03ms     1.11  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'lower')
+     1.57±0.03ms      1.74±0.01ms     1.11  rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'linear')
+     1.86±0.03ms      2.05±0.02ms     1.10  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'nearest')
+     2.62±0.02ms      2.89±0.07ms     1.10  rolling.Methods.time_rolling('Series', 10, 'float', 'max')
+     2.75±0.03ms      3.03±0.01ms     1.10  rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'max')
``` cc @jreback pipelines are green thanks @phofl keep em coming! Why not consider implementing it using the Welford method?