How to get the index and column name when apply transform to a Pandas dataframe?

Question

Given two multiindex dataframes (df1 and df2), I want to group df1 and do a transformation. In this transformation, I want to add the corresponding array from df2 to df1.

import pandas and pd
import numpy as np

def do_transform(x):
    return np.add(x, df2.ix(_index_of_x, _column_name_of_x))

df1.groupby(level=[0,1]).transform(do_transform)

How to retrieve the index and column name in Pandas transform?

EDIT:

df1 and df2 have the row size, but df2 contains more columns.

Answer 1

I think a join across multiindex levels might be better?

Anyhow proceeding with a transform; to scari's question i will assume same size.

"""
# data1.csv
alpha,beta,gamma
A,1,2
A,1,4
A,2,6
B,3,8
B,3,10
B,4,12


# data2.csv
alpha,beta,gamma
A,1,20
A,1,40
A,2,60
B,3,80
B,3,100
B,4,120
"""

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')

df1.set_index(['alpha','beta'],inplace=True)
df2.set_index(['alpha','beta'],inplace=True)

def do_transform(x):
    return x + df2.loc[df2.index.isin(x.index)]

print df1.groupby(level=[0,1]).transform(lambda x: do_transform(x)).head(len(df1))

which will produce

            gamma
alpha beta       
A     1        22
      1        44
      2        66
B     3        88
      3       110
      4       132

And if you have more than one column it works fine.

import pandas as pd
import numpy as np
"""
# data1.csv
alpha,beta,gamma,omega
A,1,2,1
A,1,4,1
A,2,6,1
B,3,8,1
B,3,10,1
B,4,12,1


# data2.csv
alpha,beta,gamma,omega
A,1,20,2
A,1,40,2
A,2,60,2
B,3,80,2
B,3,100,2
B,4,120,2
"""

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')

df1.set_index(['alpha','beta'],inplace=True)
df2.set_index(['alpha','beta'],inplace=True)

def do_transform(x):
    return x + df2.loc[x.index.unique(),:]

print df1.groupby(level=[0,1]).transform(lambda x: do_transform(x)).head(len(df1))

produces:

                gamma  omega
alpha beta              
A     1        22      3
      1        44      3
      2        66      3
B     3        88      3
      3       110      3
      4       132      3