rolling.apply extremely slow
#### Code Sample, a copy-pastable example if possible

```python
import numpy as np
import pandas as pd
size = 100000
df = pd.DataFrame(
    {"A": np.random.uniform(size=size)},
    index=pd.date_range('2019-01-01', periods=size, freq='1min'))
df.rolling("1H").mean()  # 2.32 ms ± 54.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
df.rolling("1H").apply(lambda x: np.nanmean(x), raw=True)  # 2.93 s ± 68.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
df.rolling("1H").apply(lambda x: np.nanmean(x), raw=False) # 20.9 s ± 283 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
df.rolling("1H").apply(lambda x: x.iloc[0], raw=False)  # 11.2 s ± 76.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
#### Problem description
I am not sure why the builtin rolling mean 1000x faster than calling np.mean using apply but it makes it pretty useless for anything custom. What makes things worse is that I often need to use `raw=False` because I want to make use of the timestamps to scale returns and that is fully 10,000x slower!

#### Expected Output

I just want it to be faster or want a solution that allows me to approach the performance of the builtin `mean` even if it is to somehow compile some cython code...

#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]
```
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.0.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.17.2
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 39.0.1
Cython           : None
pytest           : 4.4.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.2 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.5.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
```
</details>