ENH Consistent apply output when grouping with freq
The `BinGrouper.apply` (used by the `TimeGrouper`) and `BaseGrouper.apply` (used by the `Grouper`) have different output types. To make them consistent, remove `BinGrouper.apply` and let it use the same method as the superclass `BaseGrouper`. This requires changing `BinGrouper.groupings` to return an object which looks sufficiently like a `Grouping` object for `BaseGrouper.apply` to be happy. Use a namedtuple to create an object with the needed attributes.

I think this change will only affect the output of `apply` with a custom function, and only when grouping by a `TimeGrouper` (although a `Grouper` with a `freq` specified turns into a `TimeGrouper`, so that counts).

Closes #11742 .

tests!
 I wanted to make sure it wouldn't be outright rejected first. :-) I'll write tests now.
 Tests. On the master branch, `TestGroupBy.test_timegrouper_apply_return_type_series` fails, and `TestGroupBy.test_timegrouper_apply_return_type_value` raises a `TypeError`.
 Can u have a look thru online groupby issues (there are a few!) and see if this has some similarity with any

I recall one that had to do with the dtype changing in apply (and had to do with that code u removed) so maybe this would fix another issue 
 A search through issues tagged "Groupby", or containing "TimeGrouper" and "apply", or "Grouper", "apply", and "freq" doesn't show anything that looks to me like it would be fixed by this.
 your PR might close #5839
 i'll have to look at this more, but pls add a whatsnew
 I added a What's New entry. Not sure the best way to describe / categorize this -- perhaps it belongs under bugs? 

It doesn't look like #5839 is affected. I checked to be sure, and the examples there are unchanged by this PR. 

This PR only affects `apply` operations after a single groupby involving grouping times by a `freq` argument. And even then, something like `df.groupby([pd.Grouper(freq='M', key='date')]).apply(lambda x: x.value.sum())` is unchanged. The only thing that changes is the output if you don't put the Grouper in a list (which is now consistent with the same thing using the Grouper in a list).

I scanned through the docs and didn't see any examples or instructions that would be affected by this PR.
 I am not convinced this is doing the right thing. notice the return types dtypes.

```
In [25]: df.groupby(pd.Grouper(key='date')).apply(sumfunc_series)
Out[25]: 
            sum
date           
10/10/2000   10
11/10/2000   13

In [26]: df.groupby(pd.Grouper(key='date')).apply(sumfunc_series).dtypes
Out[26]: 
sum    int64
dtype: object
```
 I'm not sure if it's doing the right thing, but this PR doesn't change the output of `df.groupby(pd.Grouper(key='date')).apply(sumfunc_series)`, and now that output type is consistent with the output of `df_dt.groupby(pd.TimeGrouper(freq='M', key='date')).apply(sumfunc_series)`.

When you suggest testing the output of `.resample(..).apply(..)`, you mean to compare the `result` to `df_dt.set_index('date').resample('M').apply(sumfunc_series)`? I would have expected that to be the same, but it's not. Also, this PR doesn't affect the output of `.resample(..).apply(..)`. I don't know enough about the `resample` code path to say if it should. Outputs:

```
In [45]: df_dt.set_index('date').resample('M').apply(sumfunc_series)
Out[45]: 
            value
date             
2000-10-31    NaN
2000-11-30    NaN

In [46]: df_dt.set_index('date').resample('M').apply(sumfunc_value)
Out[46]: 
            value
date             
2000-10-31     10
2000-11-30     13
```

Corresponding outputs using a resampling groupby, this PR:

```
In [50]: df_dt.groupby(pd.Grouper(key='date', freq='M')).apply(sumfunc_series)
Out[50]: 
            sum
date           
2000-10-31   10
2000-11-30   13

In [51]: df_dt.groupby(pd.Grouper(key='date', freq='M')).apply(sumfunc_value)
Out[51]: 
date
2000-10-31    10
2000-11-30    13
Freq: M, dtype: int64
```

Outputs on master branch:

```
In [16]: df_dt.set_index('date').resample('M').apply(sumfunc_series)
Out[16]: 
            value
date             
2000-10-31    NaN
2000-11-30    NaN

In [17]: df_dt.set_index('date').resample('M').apply(sumfunc_value)
Out[17]: 
            value
date             
2000-10-31     10
2000-11-30     13

In [18]: df_dt.groupby(pd.Grouper(key='date', freq='M')).apply(sumfunc_series)
Out[18]: 
date           
2000-10-31  sum    10
2000-11-30  sum    13
dtype: int64

In [19]: df_dt.groupby(pd.Grouper(key='date', freq='M')).apply(sumfunc_value)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-19-4ef8c22ce5dd> in <module>()
----> 1 df_dt.groupby(pd.Grouper(key='date', freq='M')).apply(sumfunc_value)
...
TypeError: cannot concatenate a non-NDFrame object
```

The test failure is in `pandas.tests.test_generic.TestDataFrame.test_to_xarray`, which doesn't look related to my change in `test_groupby.py`.
 you can rebase on master; the `to_xarray` issue is fixed
 Rebased.
 can you rebase/update (and move note to 0.18.1). and i'll take a look.
 @jreback , done.
 @jreback , I added extra detail and examples to the What's New, and rebased. Sorry for the delay.
 thanks @stephen-hoover 

some doc-comments. ping when pushed and i can look.
 @jreback , modified the docs. Let me know if this looks good.
 thanks @stephen-hoover 
