### 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.

##
Answer #2:

Old thread but thought I would share my solution with 2d extrapolation/interpolation, respecting index values, which also works on demand. Code ended up a bit weird so let me know if there is a better solution:

```
import pandas
from numpy import nan
import numpy
dataGrid = pandas.DataFrame({1: {1: 1, 3: 2},
2: {1: 3, 3: 4}})
def getExtrapolatedInterpolatedValue(x, y):
global dataGrid
if x not in dataGrid.index:
dataGrid.ix[x] = nan
dataGrid = dataGrid.sort()
dataGrid = dataGrid.interpolate(method='index', axis=0).ffill(axis=0).bfill(axis=0)
if y not in dataGrid.columns.values:
dataGrid = dataGrid.reindex(columns=numpy.append(dataGrid.columns.values, y))
dataGrid = dataGrid.sort_index(axis=1)
dataGrid = dataGrid.interpolate(method='index', axis=1).ffill(axis=1).bfill(axis=1)
return dataGrid[y][x]
print getExtrapolatedInterpolatedValue(2, 1.4)
>>2.3
```