Interpolation on DataFrame in pandas

Question

I have a DataFrame, say a volatility surface with index as time and column as strike. How do I do two dimensional interpolation? I can reindex but how do i deal with NaN? I know we can fillna(method='pad') but it is not even linear interpolation. Is there a way we can plug in our own method to do interpolation?

Answer 1

You can use DataFrame.interpolate to get a linear interpolation.

In : df = pandas.DataFrame(numpy.random.randn(5,3), index=['a','c','d','e','g'])  In : df Out:           0         1         2 a -1.987879 -2.028572  0.024493 c  2.092605 -1.429537  0.204811 d  0.767215  1.077814  0.565666 e -1.027733  1.330702 -0.490780 g -1.632493  0.938456  0.492695  In : df2 = df.reindex(['a','b','c','d','e','f','g'])  In : df2 Out:           0         1         2 a -1.987879 -2.028572  0.024493 b       NaN       NaN       NaN c  2.092605 -1.429537  0.204811 d  0.767215  1.077814  0.565666 e -1.027733  1.330702 -0.490780 f       NaN       NaN       NaN g -1.632493  0.938456  0.492695  In : df2.interpolate() Out:           0         1         2 a -1.987879 -2.028572  0.024493 b  0.052363 -1.729055  0.114652 c  2.092605 -1.429537  0.204811 d  0.767215  1.077814  0.565666 e -1.027733  1.330702 -0.490780 f -1.330113  1.134579  0.000958 g -1.632493  0.938456  0.492695 

For anything more complex, you need to roll-out your own function that will deal with a Series object and fill NaN values as you like and return another Series object.