DatetimeIndex plot converter performance
#### Code Sample, a copy-pastable example if possible

I have a detector with 432000 data points sampled at 10 Hz (12 hours of data). I want to plot the time trace using pandas.Series

```python
import pandas as pd
import time

# This is how it is in my code, but you don't have it.
# data1 = load('../170907/data/1504817777.npy')

# You could just as easily do 
data1 = numpy.random.randn(432000)

date_index = pd.date_range(start=1504817777*1e9, periods=len(data1), freq='100 ms', tz='UTC')\
    .tz_convert('America/Los_Angeles')\
    .tz_localize(None)
voltage = pd.Series(data1, date_index)

voltage[::10000].plot() # This is only 5 points, but it takes a **long** time to render this
```
#### Problem description

I *love* that pandas handles a lot of the date handling automatically. However, it's not practical to spend 30 seconds waiting for this plot to render. I've tried striding over the data in order to decrease the amount of time (I don't need to see all 432000 points), but this doesn't seem to improve rendering time.

Am I missing something?

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

<details>


INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 36.2.7
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.6.2
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0b10
httplib2: 0.10.3
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
boto: None
pandas_datareader: None


</details>
