.unique() inconsistency between Series and Index objects
#### A small, complete example of the issue

``` python
ix = pd.Index([0,0,1,2])
sr = pd.Series([0,0,1,2])

ix.unique()
Int64Index([0, 1, 2], dtype='int64')

sr.unique()
array([0, 1, 2], dtype=int64)

```
#### Expected Output

I'd expect that unique returns the original datatype or always an array.
I'd prefer the original data_type since arrays do not provide a to_series function but indexes do.
So when working with pipelines, this is much more convenient
#### Output of `pd.show_versions()`

<details>
## INSTALLED VERSIONS

commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.0
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None

</details>
