Concatenate Pandas columns under new multi-index level

Question

Given a dictionary of data frames like:

dict = {\'ABC\': df1, \'XYZ\' : df2}   # of any length...

where each data frame has the same column

Answer 1

You can do it with concat (the keys argument will create the hierarchical columns index):

d = {'ABC' : df1, 'XYZ' : df2}
print pd.concat(d.values(), axis=1, keys=d.keys())


                XYZ                                          ABC           \
               Open     High      Low    Close   Volume     Open     High   
Date                                                                        
2002-01-17  0.18077  0.18800  0.16993  0.18439  1720833  0.18077  0.18800   
2002-01-18  0.18439  0.21331  0.18077  0.19523  2027866  0.18439  0.21331   
2002-01-21  0.19523  0.20970  0.19162  0.20608   771149  0.19523  0.20970   


                Low    Close   Volume  
Date                                   
2002-01-17  0.16993  0.18439  1720833  
2002-01-18  0.18077  0.19523  2027866  
2002-01-21  0.19162  0.20608   771149

Really concat wants lists so the following is equivalent:

print(pd.concat([df1, df2], axis=1, keys=['ABC', 'XYZ']))