API: Interpolate at new values
First time I used the .interpolate() method I thought that it receives a new index and then interpolates on it, similar to [scipy.interpolate.interp1d](http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d)
From scipy web:

``` python
from scipy import interpolate
x = np.arange(0, 10)
y = np.exp(-x/3.0)
f = interpolate.interp1d(x, y)
xnew = np.arange(0,9, 0.1)
ynew = f(xnew)   # use interpolation function returned by `interp1d`
```

Later I saw the .reindex() method, so I understood that this role is done by .reindex(). However .reindex() is not really doing a powerful interpolation, just extending the current values using the [method](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.reindex.html) keyword.

The current way to achieve it (joining previous and new index and then using .reindex()), in version 0.15.0,

``` python
index_joined = df.index.join(new_index, how='outer')
df.reindex(index=index_joined).interpolate().reindex(new_index)
```

A simpler syntax could be accepting the methods from [.interpolate()](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.interpolate.html) into the 'method' keyword of .reindex():

``` python
df.reindex(index=new_index, method='linear')
```
