as_index =True for groupby transform  inconsistent with agg
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import numpy as np
index = pd.date_range('10/1/1999', periods=1100, name='Date')

ts = pd.DataFrame({'Entry': np.random.normal(300, 50, 1100), 
                                'Exit':  np.random.normal(300, 50, 1100)}, index)

ts['Year'] = ts.index.year
ts['Day'] = ts.index.weekday_name

zscore = lambda x: (x - x.mean()) / x.std()
ts.groupby(['Year', 'Day'],  as_index=True).transform(lambda g:g).head(5)
```
#### Problem description

Unlike agg, when using transform
1. as_index =True does not set the index to the groupby by (i.e ['Year', 'Day']). 
2. as_index = False does not prepend the groupby by column to the output of the transform as is done with agg.

I found this inconsistency very confusing and hard to work with. 

This is also noted in ##5755
#### Expected Output

If as_index =True, the index of the result should the groupby by (i.e ['Year', 'Day']). 

if as_index =False, the output should have the the groupby by (i.e ['Year', 'Day']) prepended to the as is done with agg.

I've really struggled using transform, partly as the semantics of transform were unclear to me. Specifically :
1. Which columns is the transform applied to. Is it all columns of the df or only those not in the groupby by? It looks like its the latter. This seems to be true with agg too. It would have been helpful to me if this was clearer in the documentation

2. Is the transform passed a df of columns or is it applied column by column? My guess is it's the former if the transform takes a df otherwise it's the latter. This seems to be hinted at in some parts of the documentation but it could be made more explicit.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.41-36.55.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
