Inconsistent BBox dimensions when plotting different sized time series.
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as plt_dates
idx = pd.to_datetime(['2016-10-17', '2016-10-18', '2016-10-19', '2016-10-20',
               '2016-10-21', '2016-10-24', '2016-10-25', '2016-10-26',
               '2016-10-27', '2016-10-28', '2016-10-31', '2016-11-01',
               '2016-11-02', '2016-11-03', '2016-11-04', '2016-11-07',
               '2016-11-08', '2016-11-09', '2016-11-10', '2016-11-11',
               '2016-11-14', '2016-11-15', '2016-11-16', '2016-11-17',
               '2016-11-18', '2016-11-21', '2016-11-22', '2016-11-23',
               '2016-11-25', '2016-11-28', '2016-11-29', '2016-11-30',
               '2016-12-01', '2016-12-02', '2016-12-05', '2016-12-06',
               '2016-12-07', '2016-12-08', '2016-12-09', '2016-12-12',
               '2016-12-13', '2016-12-14', '2016-12-15', '2016-12-16',
               '2016-12-19', '2016-12-20', '2016-12-21', '2016-12-22',
               '2016-12-23', '2016-12-27', '2016-12-28', '2016-12-29',
               '2016-12-30', '2017-01-03', '2017-01-04', '2017-01-05',
               '2017-01-06', '2017-01-09', '2017-01-10', '2017-01-11',
               '2017-01-12', '2017-01-13', '2017-01-17', '2017-01-18',
               '2017-01-19', '2017-01-20', '2017-01-23', '2017-01-24',
               '2017-01-25', '2017-01-26', '2017-01-27', '2017-01-30',
               '2017-01-31', '2017-02-01', '2017-02-02', '2017-02-03',
               '2017-02-06', '2017-02-07', '2017-02-08', '2017-02-09',
               '2017-02-10', '2017-02-13', '2017-02-14', '2017-02-15',
               '2017-02-16', '2017-02-17', '2017-02-21', '2017-02-22',
               '2017-02-23', '2017-02-24', '2017-02-27', '2017-02-28',
               '2017-03-01', '2017-03-02', '2017-03-03', '2017-03-06',
               '2017-03-07', '2017-03-08', '2017-03-09', '2017-03-10'])
values= range(len(idx))

df = pd.Series(data=values, index=idx)
### First plot the entire set. 
ax=df.plot()

dataLim_100_days = ax.dataLim

ax.xaxis.set_minor_locator(plt_dates.AutoDateLocator())        
ax.xaxis.set_minor_formatter(plt_dates.DateFormatter('%d %Y'))
ax.xaxis.set_major_formatter(plt_dates.DateFormatter('%b %d %Y'))
plt.show()
## Now plot with only 2 items 
ax=df[:2].plot()
dataLim_2_days = ax.dataLim

ax.xaxis.set_minor_locator(plt_dates.AutoDateLocator())            
ax.xaxis.set_minor_formatter(plt_dates.DateFormatter('%d %Y'))
ax.xaxis.set_major_formatter(plt_dates.DateFormatter('%b %d %Y'))
plt.show()

### Range where the DataLim's are off. 

ax=df[:3].plot()
dataLim_3_days = ax.dataLim

ax.xaxis.set_minor_locator(plt_dates.AutoDateLocator())            
ax.xaxis.set_minor_formatter(plt_dates.DateFormatter('%d %Y'))
ax.xaxis.set_major_formatter(plt_dates.DateFormatter('%b %d %Y'))
plt.show()


ax=df[:28].plot()

dataLim_28_days =ax.dataLim
ax.xaxis.set_minor_locator(plt_dates.AutoDateLocator())            
ax.xaxis.set_minor_formatter(plt_dates.DateFormatter('%d %Y'))
ax.xaxis.set_major_formatter(plt_dates.DateFormatter('%b %d %Y'))
plt.show()

print(dataLim_100_days, dataLim_2_days, dataLim_3_days, dataLim_28_days)
  # %%
```
#### Problem description

When plotting timeseries charts with pandas and matplot lib. 
When the series has between 3 and 28 objects the ax reports incorrect BBox values that are off by a few orders of magnitude. 
In the code above you can see that the first 2 items have x0 values around 736000 
While the later 2, are in the 10000's. 

This causes issues when using matplotlib.date.AutoDateLocator which expects x values to be in seconds from epoch, as you can see on the chart output above dates get stamped around year 0034 when they should be around 2017. 

It also causes problems when inserting text by indexing from the date on the chart. 
Such as an error 


#### Expected Output

Bbox values for x should be consistent across different sizes of timeseries objects. 
As we see in dataLim_100_days and dataLim_2_days examples above. 

#### 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: en
LOCALE: None.None

pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
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: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: 0.2.1


</details>
