pandas multiindex dataframe, ND interpolation for missing values

Question

Is it possible in pandas to interpolate for missing values in multiindex dataframe. This example below does not work as expected:

arr1=np.array(np.arange(1.,         


        

Answer 1

def multireindex(_df, new_multi_index, method='linear',copy=True):
    #from scipy.interpolate import griddata
    #import numpy as np
    #import pandas as pd
    _points=np.array(_df.index.values.tolist())
    dfn=dict()
    for aclm in _df.columns:
        dfn[aclm] = griddata(_points, _df[aclm], 
                        np.array(new_multi_index), method=method)
    dfn=pd.DataFrame(dfn,index=pd.MultiIndex.from_tuples(
            new_multi_index,names=_df.index.names))
    return pd.concat([dfn,_df])

import pandas as pd
import numpy as np
#import numpy.random as npr
#df1=pd.DataFrame(npr.rand(10,5))
arr1=np.random.rand(100)
arr2=np.random.rand(100)
arr1,arr2=[np.round(a*b) for a,b in 
                zip([arr1,arr2],[100,100,1000])]
df1=pd.DataFrame(zip(arr1,arr2,arr1+arr2,arr1*arr2),columns=['x','y','plus','times'])
df1.set_index(['x','y'],inplace=True)
from scipy.interpolate import griddata
new_points=[(20.0,20.0),(25.0,25.0)]
df2=multireindex(df1,new_points)
df2.head()