ENH: Richer options for `interpolate` and `resample`
related #1892, #1479

Is there any interest in giving interpolate and resample (to higher frequency) some additional methods?

For example:

``` python
from scipy import interpolate
df = pd.DataFrame({'A': np.arange(10), 'B': np.exp(np.arange(10) + np.random.randn())})
xnew = np.arange(10) + .5

In [46]: df.interpolate(xnew, method='spline')
```

Could return something like

``` python
In [47]: pd.DataFrame(interpolate.spline(df.A, df.B, xnew, order=4), index=xnew)
Out[47]: 
               0
0.5     1.044413
1.5     0.798392
2.5     3.341909
3.5     8.000314
4.5    22.822819
5.5    60.957659
6.5   166.844351
7.5   451.760621
8.5  1235.969910
9.5     0.000000  # falls outside the original range so interpolate.spline sets it to 0.
```

I have never used the DataFrame's interpolate, but a quick glance says that something like the above wouldn't be backwards compatible with the current calling convention.  Maybe a different name?  This may be confusing two issues: interpolating over missing values and interpolating / predicting non-existent values.  Or are they similar enought that they can be treated the same.  I would think so.

These are just some quick thoughts before I forget.  I haven't spent much time thinking a design through yet. I'd be happy to work on this in a month or so.

Also does this fall in the realm of statsmodels?  
