Enhancement Request: control extrapolation on .interpolate
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import numpy as np

dfMain = pd.DataFrame({
    'a': [0, 1, np.NAN, 3, 4],
    'b': [np.NaN, np.NaN, np.NaN, 3, 4],
    'c': [0 , 1, 2, 3, np.NaN]})

for col in dfMain:
    start = dfMain[col].first_valid_index()
    end = dfMain[col].last_valid_index()
    dfMain.loc[start:end, col] = dfMain.loc[start:end, col].interpolate()

print(dfMain)
```

#### Problem description
It would be very nice to have a limit_direction='inside' that would make interpolate only fill values that are surrounded (both in front and behind) with valid values.

This would allow an interpolate to only fill missing values in a series and **not extend** the series beyond its original limits.  The key here is that it is sometimes important to maintain the original range of a series, but still fill in the gaps.

The example shows a simple DataFrame with an 'inside' interpolation.

#### Expected Output

```python
     a    b    c
0  0.0  NaN  0.0
1  1.0  NaN  1.0
2  2.0  NaN  2.0
3  3.0  3.0  3.0
4  4.0  4.0  NaN
```
#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-75-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.4.1
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: 0.2.1
None


</details>
