Pandas optional argument to interpolation function for interpolating on new columns or indices

Here is a workaround how to achieve this manually:

import pandas as pd
import numpy as np
from numpy.random import rand

dfPP=pd.DataFrame(rand(3,5),columns=rand(5),index=rand(3))
nanarr=np.empty(dfPP.shape[0])
nanarr[:]=np.NAN
new_point=dfPP.columns.values.mean()
dfPP.loc[:,new_point]=nanarr
dfPP.sort(axis=1,inplace=True)
dfPP.head()

	0.211451507884	0.574409309122	0.626609642735	0.728622716072	0.752814445643	0.865750234955
0.340822	0.451636	0.292137	NaN	0.162465	0.481506	0.384237
0.996621	0.997825	0.957920	NaN	0.728265	0.916599	0.286137
0.711955	0.091902	0.951715	NaN	0.363268	0.344484	0.661254

dfPP=dfPP.interpolate(method='values',axis=1)
dfPP.head()

	0.211451507884	0.574409309122	0.626609642735	0.728622716072	0.752814445643	0.865750234955
0.340822	0.451636	0.292137	0.248244	0.162465	0.481506	0.384237
0.996621	0.997825	0.957920	0.880183	0.728265	0.916599	0.286137
0.711955	0.091902	0.951715	0.752529	0.363268	0.344484	0.661254

Comment From: jreback

pls provide an example of what you are looking in the top of the issue

Comment From: den-run-ai

provided the example above, so the new optional arguments are the following:

DataFrame.interpolate(new_columns=[x,y,z], new_rows=[a,b,c], _args, *_kwargs)

Comment From: mroeschke

Thanks for the suggestion, but since there hasn't been much community or core dev interest in years going to close. Happy to reopen if there's renewed interest