Interpolating Multi Index A Pandas Dataframe

I need to interpolate multi index dataframe: for example: this is the main dataframe: a b c result 1 1 1 6 1 1 2 9 1 2 1 8 1 2 2 11 2

Solution 1:

You can use scipy.interpolate.LinearNDInterpolator to do what you want. If the dataframe is a MultiIndex with the column 'a','b' and 'c', then:

from scipy.interpolate import LinearNDInterpolator as lNDI
print (lNDI(points=df.index.to_frame().values, values=df.result.values)([1.3, 1.7, 1.55]))

now if you have dataframe with all the tuples (a, b, c) as index you want to calculate, you can do for example:

def pd_interpolate_MI (df_input, df_toInterpolate):
    from scipy.interpolate import LinearNDInterpolator as lNDI
    #create the functionof interpolation
    func_interp = lNDI(points=df_input.index.to_frame().values, values=df_input.result.values)
    #calculate the valuefor the unknown index
    df_toInterpolate['result'] = func_interp(df_toInterpolate.index.to_frame().values)
    #return the dataframe with the newvaluesreturn pd.concat([df_input, df_toInterpolate]).sort_index()

Then for example with your df and df_toI = pd.DataFrame(index=pd.MultiIndex.from_tuples([(1.3, 1.7, 1.55),(1.7, 1.4, 1.9)],names=df.index.names)) then you get

print (pd_interpolate_MI(df, df_toI))
              result
a   b   c           
1.0 1.0 1.00    6.00
        2.00    9.00
    2.0 1.00    8.00
        2.00   11.00
1.3 1.7 1.55    9.35
1.7 1.4 1.90   10.20
2.0 1.0 1.00    7.00
        2.00   10.00
    2.0 1.00    9.00
        2.00   12.00