Series.resample spends a lot of time calling is_unique
#### Code Sample, a copy-pastable example if possible

```
x = np.arange(100000).astype(float)
t =  pd.DatetimeIndex([time.time()]) + x * np.timedelta64(1, 'ms')
%prun pd.Series(x, t).resample('1s').first()
         849 function calls (829 primitive calls) in 0.023 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.008    0.008    0.008    0.008 base.py:938(is_unique)
```
#### Problem description

I have profiled some performance critical code and resample is taking a relatively large percentage of the time, which was as expected.  However I noticed that resample is actually spending a lot of time checking that the index is unique, which was not expected.  In my particular use case I know that the index is unique and checking that is wasted effort.  Attached is a simple example.  I suggest allowing the user to declare that the index is unique (and maybe sorted too) to avoid explicit checks, since I suspect that a large number of use cases have known unique indexes.  Perhaps is_unique could be a settable attribute, potentially allowing it to be streamlined in many different places.  Thank you

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

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
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: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
