df.interpolate limit_direction= does not work without limit=x setting
the interpolate limit_direction setting should work even without a limit=x setting.

When interpolate is called without a limit=x setting, it always (apparently) uses the limit_direction setting default of 'forward' even when it is explicitly set to 'backward' or 'both'

The code below will show 3 DataFrames.

- The first shows the initial before interpolation.
- The second shows the interpolation done correctly in the backward direction.
- The third shows the dataframe done incorrectly in the 'forward' direction even though the limit_direction='backward' was used. 

The docs do not mention a linkage between the two settings, but if this is expected behavior (I hope not), it would be oh so nice to raise an exception like the existing error for a bad limit_direction setting.

Many thanks for all your work!

#### Example Code:

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

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

print("initial df")
print(dfMain)

print("backwards with limit=1 (correct result):")
print(dfMain.interpolate(limit_direction='backward', limit=1))

print("backwards no longer works when limit is removed (it is going forward):")
print(dfMain.interpolate(limit_direction='backward'))
```

#### 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
initial df


</details>
