API: rolling.apply will pass Series to function
closes #5071

Thoughts on the default being ndarray with a warning that the default will change to `raw=False` in the future? Then this isn't a breaking change. yeah i could make the keyword default (of None) and then warn that it’s going to change

that’s back compat, though would have to flip it at some point anyhow # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/20584?src=pr&el=h1) Report
> Merging [#20584](https://codecov.io/gh/pandas-dev/pandas/pull/20584?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/d04b7464dcc20051ef38ac2acda580de854d3e01?src=pr&el=desc) will **decrease** coverage by `<.01%`.
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/20584/graphs/tree.svg?token=eZ4WkYLtcO&src=pr&width=650&height=150)](https://codecov.io/gh/pandas-dev/pandas/pull/20584?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #20584      +/-   ##
==========================================
- Coverage   91.84%   91.83%   -0.01%     
==========================================
  Files         153      153              
  Lines       49279    49295      +16     
==========================================
+ Hits        45259    45272      +13     
- Misses       4020     4023       +3
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.23% <100%> (-0.01%)` | :arrow_down: |
| #single | `41.89% <23.07%> (-0.01%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/20584?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/generic.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ2VuZXJpYy5weQ==) | `95.94% <ø> (ø)` | :arrow_up: |
| [pandas/core/window.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvd2luZG93LnB5) | `96.29% <100%> (+0.02%)` | :arrow_up: |
| [pandas/core/arrays/categorical.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvYXJyYXlzL2NhdGVnb3JpY2FsLnB5) | `95.78% <0%> (-0.41%)` | :arrow_down: |
| [pandas/util/testing.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL3V0aWwvdGVzdGluZy5weQ==) | `84.38% <0%> (-0.21%)` | :arrow_down: |
| [pandas/io/pytables.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2lvL3B5dGFibGVzLnB5) | `92.41% <0%> (-0.05%)` | :arrow_down: |
| [pandas/core/internals.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvaW50ZXJuYWxzLnB5) | `95.53% <0%> (-0.01%)` | :arrow_down: |
| [pandas/core/groupby/groupby.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ3JvdXBieS9ncm91cGJ5LnB5) | `92.55% <0%> (ø)` | :arrow_up: |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `97.16% <0%> (ø)` | :arrow_up: |
| [pandas/core/indexing.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvaW5kZXhpbmcucHk=) | `93.08% <0%> (+0.05%)` | :arrow_up: |
| [pandas/core/dtypes/missing.py](https://codecov.io/gh/pandas-dev/pandas/pull/20584/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZHR5cGVzL21pc3NpbmcucHk=) | `92.85% <0%> (+1.78%)` | :arrow_up: |

------

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 this is all fixed up. now back-compat (with a warning). if anyone wants to look 

cc @WillAyd 
@TomAugspurger  lgtm While trying this out, I also noticed that rolling apply only seems to work on numeric (int/float) data. Is this a known limitation/bug? (didn't directly find an issue about it):

```python
In [69]: s = pd.Series(list('abcd'))

In [71]: s.rolling(2).apply(lambda x: x+1, raw=True)   
Out[71]: 
0    a         #<-------- for string data, just returns the original series, whathever the function is
1    b
2    c
3    d
dtype: object

In [72]: s = pd.Series(pd.Categorical(list('abcd')))

In [73]: s.rolling(2).apply(lambda x: x+1, raw=True)
Out[73]: 
[a, b, c, d]     #<-------- for categorical data, just returns the original Categorical (not even series)
Categories (4, object): [a, b, c, d]

In [74]: s = pd.Series(pd.timedelta_range("1 days", periods=4))

In [75]: s.rolling(2).apply(lambda x: x+1, raw=True)   #<----- for timedelta/datetime, raises informative error
...
NotImplementedError: ops for Rolling for this dtype timedelta64[ns] are not implemented
```

(this is a separate issue as this PR, it's already like this in released version) any comments. @jreback can you *please* work with additional commits, that makes a second review round really a lot easier for me updated, I adjusted the ``.aggregate`` doc-string & put in an api-note. lmk if any further comments. bombs away Thanks! @jreback hello! I googled insanely, but now I'm even more confused. Is there a way to pass 2D with few columns instead of 1D series to apply(function)? There was a function rolling_apply, but it's depricated. Ended up writing my own function with cycle inside, which emulates rolling window with some cross-column calculations, but it's rather slow. Thank you.