groupby.transform() fails when performed on column of numpy arrays 
#### Code Sample, a copy-pastable example if possible

```python

df = pd.DataFrame.from_dict({"A":[1, 1, 2, 2],
                            "B":[np.array([1, 2, 3, 4]),
                                np.array([5, 6, 7, 8]),
                                np.array([1, 2, 3, 4]),
                                np.array([5, 6, 7, 8])]})

def mean_of_arrays(x):
    """
    Returns the mean of the two arrays.
    """
    print((x.iloc[0] + x.iloc[1])/2)
    return (x.iloc[0] + x.iloc[1])/2

df.groupby(["A"])["B"].transform(mean_of_arrays)
# Print [3, 4, 5, 6], [3, 4, 5, 6], as expected...
# But returns [3, 4, 3, 4]

```
#### Problem description

When a column contains a list of numpy arrays, and a "transform" operation is completed on those lists,
the lists are not returned by the transform operation. Instead, the n-th element of the list is return, where n is the number of elements for each group.

#### Expected Output

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.8.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.4
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
boto: 2.46.1
pandas_datareader: None

</details>
