Performance increase rolling min max
- [x] closes #19521 
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

In my testing the performance of 

```python
import pandas as pd
import timeit

df = pd.DataFrame({"a": 0}, index=pd.date_range('2017-01-01', '2019-01-01', freq='1T'))

timeit.timeit(lambda: df.rolling('1d').max(), number=1)
```

Went from 1.8 sec to 0.3 sec on my machine (lenovo laptop).
Hello @hexgnu! Thanks for updating the PR.

 - In the file [`asv_bench/benchmarks/rolling.py`](https://github.com/pandas-dev/pandas/blob/65c0dbe757f9c7b8b138c6627070bccaaddc0f76/asv_bench/benchmarks/rolling.py), following are the PEP8 issues :

> [Line 25:1](https://github.com/pandas-dev/pandas/blob/65c0dbe757f9c7b8b138c6627070bccaaddc0f76/asv_bench/benchmarks/rolling.py#L25): [E302](https://duckduckgo.com/?q=pep8%20E302) expected 2 blank lines, found 1





##### Comment last updated on February 12, 2018 at 04:38 Hours UTC pls add a whatsnew, and I think we have asv's for this, so run and post those as well (of course need passing too :>)

approach looks good. # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/19549?src=pr&el=h1) Report
> Merging [#19549](https://codecov.io/gh/pandas-dev/pandas/pull/19549?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/7a5634e79f0b42ddbf602720dc184e9ce69f929c?src=pr&el=desc) will **increase** coverage by `<.01%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/19549/graphs/tree.svg?width=650&height=150&token=eZ4WkYLtcO&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/19549?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #19549      +/-   ##
==========================================
+ Coverage   91.58%   91.59%   +<.01%     
==========================================
  Files         150      150              
  Lines       48807    48807              
==========================================
+ Hits        44702    44704       +2     
+ Misses       4105     4103       -2
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `89.96% <ø> (ø)` | :arrow_up: |
| #single | `41.73% <ø> (ø)` | :arrow_up: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/19549?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/util/testing.py](https://codecov.io/gh/pandas-dev/pandas/pull/19549/diff?src=pr&el=tree#diff-cGFuZGFzL3V0aWwvdGVzdGluZy5weQ==) | `83.85% <0%> (+0.2%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/19549?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/19549?src=pr&el=footer). Last update [7a5634e...65c0dbe](https://codecov.io/gh/pandas-dev/pandas/pull/19549?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 Ok pretty sure this will pass this time since I went through the trouble of setting up windows and testing it on MSVC 9.0. I'm not sure it matters in practice with the toolchains that actually get used but historically we haven't used c++ at all inside pandas - maybe has implications adding it? Here are the ASVs I ran. But I'm noticing 1 an increase in time_pairwise and also idk if there's a variable window rolling benchmark. So I'll look into that.
```
       before           after         ratio
     [36f90528]       [42f8fdfd]
+      5.86±0.2ms       7.29±0.6ms     1.24  rolling.Pairwise.time_pairwise(10, 'corr', False)
-     2.30±0.08ms      2.08±0.03ms     0.91  rolling.Methods.time_rolling('Series', 1000, 'float', 'mean')
-        38.7±2ms       33.4±0.6ms     0.86  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'median')
-      3.56±0.3ms      2.88±0.03ms     0.81  rolling.Quantile.time_quantile('Series', 10, 'float', 1)
-      3.46±0.2ms       2.80±0.2ms     0.81  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0)
-      3.79±0.2ms      2.95±0.07ms     0.78  rolling.Methods.time_rolling('Series', 10, 'float', 'min')
-      2.61±0.2ms      1.98±0.03ms     0.76  rolling.Methods.time_rolling('Series', 10, 'int', 'sum')
-        73.3±5ms         55.7±1ms     0.76  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'median')
-      3.96±0.2ms      2.77±0.07ms     0.70  rolling.Quantile.time_quantile('Series', 1000, 'float', 0)
-      4.69±0.6ms       3.07±0.1ms     0.65  rolling.Methods.time_rolling('Series', 10, 'float', 'std')
-      9.06±0.3ms       5.49±0.2ms     0.61  rolling.Methods.time_rolling('Series', 10, 'int', 'count')
-      3.63±0.2ms      2.13±0.08ms     0.59  rolling.Methods.time_rolling('Series', 10, 'float', 'mean')
-        9.37±1ms       5.42±0.4ms     0.58  rolling.Methods.time_rolling('Series', 10, 'float', 'count')
-      3.96±0.4ms      2.27±0.08ms     0.57  rolling.Methods.time_rolling('Series', 10, 'int', 'kurt')
-     3.55±0.08ms      2.00±0.03ms     0.56  rolling.Methods.time_rolling('Series', 10, 'float', 'sum')
-      4.22±0.2ms       2.17±0.3ms     0.51  rolling.Methods.time_rolling('Series', 10, 'int', 'skew')
-      5.75±0.4ms      2.89±0.03ms     0.50  rolling.Methods.time_rolling('Series', 10, 'float', 'max')
-        4.27±1ms      2.11±0.04ms     0.49  rolling.Methods.time_rolling('Series', 1000, 'int', 'min')
``` we have other c++ code so this is not a big deal @chris-b1 also there is C++ code in the repo look at msgpack So in reading this most of the benchmarks for variable rolling windows has gone down but some haven't. How accurate is ASV? Or is it perhaps me doing something else impacting it, cause I was doing some work in a jupyter notebook.

```
      before           after         ratio
     [36f90528]       [060dfb77]
+      4.78±0.1ms       10.5±0.8ms     2.19  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'min')
+      3.03±0.2ms       5.99±0.7ms     1.98  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'min')
+      3.66±0.1ms       7.09±0.6ms     1.94  rolling.Quantile.time_quantile('Series', 10, 'float', 1)
+      2.26±0.1ms       4.34±0.7ms     1.92  rolling.Methods.time_rolling('Series', 10, 'int', 'sum')
+         129±3ms          159±5ms     1.24  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'median')
+     4.74±0.07ms       5.61±0.3ms     1.18  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'kurt')
-        395±50ms          340±4ms     0.86  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0.5)
-      5.76±0.1ms       4.90±0.3ms     0.85  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'std')
-      6.17±0.2ms      5.10±0.06ms     0.83  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'skew')
-      4.20±0.2ms      3.47±0.07ms     0.83  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'sum')
-      23.9±0.9ms       19.2±0.8ms     0.80  rolling.Pairwise.time_pairwise(1000, 'cov', True)
-      4.08±0.4ms      3.20±0.05ms     0.78  rolling.Quantile.time_quantile('Series', 1000, 'float', 1)
-        6.22±1ms       4.72±0.1ms     0.76  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'kurt')
-        4.46±1ms      3.21±0.08ms     0.72  rolling.Methods.time_rolling('Series', 1000, 'float', 'kurt')
-      4.12±0.3ms       2.91±0.2ms     0.71  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1)
-        223±20ms          152±9ms     0.68  rolling.Quantile.time_quantile('Series', 10, 'float', 0.5)
-      6.47±0.7ms       4.42±0.2ms     0.68  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'skew')
-        4.90±1ms       3.28±0.2ms     0.67  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'count')
-        11.2±1ms         7.40±1ms     0.66  rolling.Pairwise.time_pairwise(None, 'corr', False)
-        7.08±2ms       4.47±0.2ms     0.63  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'skew')
-      5.83±0.7ms       3.66±0.3ms     0.63  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'mean')
-      4.41±0.4ms       2.71±0.3ms     0.61  rolling.Quantile.time_quantile('Series', 10, 'int', 0)
-        5.22±1ms      3.16±0.07ms     0.60  rolling.Methods.time_rolling('Series', 1000, 'float', 'skew')
-        8.02±1ms       4.57±0.2ms     0.57  rolling.Pairwise.time_pairwise(1000, 'cov', False)
-      5.74±0.5ms      3.13±0.07ms     0.55  rolling.Methods.time_rolling('Series', 1000, 'float', 'max')
-      3.73±0.4ms       1.92±0.2ms     0.52  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'sum')
-      3.76±0.3ms       1.88±0.2ms     0.50  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1)
-      4.55±0.5ms      2.25±0.06ms     0.49  rolling.Quantile.time_quantile('Series', 1000, 'int', 1)
-      4.62±0.6ms      2.27±0.01ms     0.49  rolling.Methods.time_rolling('Series', 1000, 'float', 'mean')
-      7.25±0.5ms       3.48±0.2ms     0.48  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'count')
-      6.37±0.3ms       2.84±0.2ms     0.45  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0)
-        325±20ms         138±10ms     0.43  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'median')
-        821±20ms          330±5ms     0.40  rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5)
-        98.9±5ms         6.45±1ms     0.07  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'max')
-         102±3ms       6.56±0.4ms     0.06  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'min')
-         107±5ms       5.99±0.4ms     0.06  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'max')
-         117±2ms       6.44±0.6ms     0.06  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'min')
-         104±6ms       5.11±0.2ms     0.05  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'max')
-         106±2ms       5.09±0.1ms     0.05  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'max')
-       117±0.6ms       5.04±0.4ms     0.04  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'min')
-         126±5ms       5.35±0.1ms     0.04  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'min')
-           2.26s       7.99±0.8ms     0.00  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'max')
-           2.29s         6.95±1ms     0.00  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'max')
-           2.90s         8.39±2ms     0.00  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'min')
-           2.09s       5.58±0.3ms     0.00  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'min')
-           2.52s         6.63±1ms     0.00  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'max')
-           2.41s         6.09±1ms     0.00  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'min')
-           2.45s       5.60±0.2ms     0.00  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'min')
-           2.61s       5.63±0.1ms     0.00  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'max')

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
``` can you add a whatsnew note in perf. lgtm otherwise.

I suspect that a very short time window has a small perf regression here, but we win really big for bigger windows. So this is a nice tradeoff (in theory we could use the original method for short windows but that adds a lot to code complexity). You could add this in the code as a comment. i restarted. ping on green. thanks @hexgnu nice patch!