Concatenate Pandas columns under new multi-index level

Given a dictionary of data frames like:

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

where each data frame has the same columns and similar index, for example:

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

What is the simplest way to combine all the data frames into one, with a multi-index like:

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

I've tried a few methods - eg for each data frame replace the columns with a multi-index like .from_product(['ABC', columns]) and then concatenate along axis=1, without success.

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']))

Add a symbol column to your dataframes and set the index to include the symbol column, concat and then unstack that level:

The following assumes that there are as many symbols as DataFrames in your dict, and also that you check that the order of symbols is as you want it based on the order of the dict keys:

DF_dict = {'ABC': df1, 'XYZ' : df2} 
dict_keys = DF_dict.keys()
symbols = ['ABC', 'ZXY']

for x in xrange(len(symbols)):
	DF_dict[dict_keys[x]]['symbol'] = symbols[x]
	DF_dict[dict_keys[x]].reset_index(inplace = True)
	DF_dict[dict_keys[x]].set_index(['symbol', 'Date'], inplace = True)
	
DF = pd.concat(DF_dict[df] for df in dict_keys)
DF = DF.unstack('symbol')

I think that would be the approach I would take. Some people are against the inplace syntax. I use it here only as convenience.

pandas.concat does this automagically now:

import pandas as pd

index = ["row1", "row2"]
df_a = pd.DataFrame({"foo": range(0, 2), "bar": range(2, 4)}, index)
df_b = pd.DataFrame({"foo": range(4, 6), "bar": range(6, 8)}, index)

pd.concat({"A": df_a, "B": df_b}, axis=1)  # axis="columns" also works

       A       B    
     foo bar foo bar
row1   0   2   4   6
row2   1   3   5   7