Combine Pandas DataFrames while creating MultiIndex Columns - pandas

Question

I have two DataFrames, something like this:
import pandas as pd
dates = pd.Index(['2016-10-03', '2016-10-04', '2016-10-05'], name='Date')
close = pd.DataFrame( {'AAPL': [112.52, 113., 113.05],
'CSCO': [ 31.5, 31.35, 31.59 ],
'MSFT': [ 57.42, 57.24, 57.64 ] }, index = dates )
volume= pd.DataFrame( {'AAPL': [21701800, 29736800, 21453100] ,
'CSCO': [14070500, 18460400, 11808600] ,
'MSFT': [19189500, 20085900, 16726400] }, index = dates )
The output of DataFrame 'close' looks like this:
AAPL CSCO MSFT
Date
2016-10-03 112.52 31.50 57.42
2016-10-04 113.00 31.35 57.24
2016-10-05 113.05 31.59 57.64
And the output of DataFrame 'volume' looks like this:
AAPL CSCO MSFT
Date
2016-10-03 21701800 14070500 19189500
2016-10-04 29736800 18460400 20085900
2016-10-05 21453100 11808600 16726400
I would like to combine these two DataFrames into a single DataFrame with MultiIndex COLUMNS so that it looks like this:
AAPL CSCO MSFT
Close Volume Close Volume Close Volume
Date
2016-10-03 112.52 21701800 31.50 14070500 57.42 19189500
2016-10-04 113.00 29736800 31.35 18460400 57.24 20085900
2016-10-05 113.05 21453100 31.59 11808600 57.64 16726400
Can anyone give me an idea how to do that? I've been playing with pd.concat and pd.merge, but it's not clear to me how to get it to line up on the date index and allow me to provide names for the sub-index ('Close" and 'Volume') on the columns.

Answer 1

You can use the keys kwarg of concat:
In [11]: res = pd.concat([close, volume], axis=1, keys=["close", "volume"])
In [12]: res
Out[12]:
close volume
AAPL CSCO MSFT AAPL CSCO MSFT
Date
2016-10-03 112.52 31.50 57.42 21701800 14070500 19189500
2016-10-04 113.00 31.35 57.24 29736800 18460400 20085900
2016-10-05 113.05 31.59 57.64 21453100 11808600 16726400
With a little rearrangement:
In [13]: res.swaplevel(0, 1, axis=1).sort_index(axis=1)
Out[13]:
AAPL CSCO MSFT
close volume close volume close volume
Date
2016-10-03 112.52 21701800 31.50 14070500 57.42 19189500
2016-10-04 113.00 29736800 31.35 18460400 57.24 20085900
2016-10-05 113.05 21453100 31.59 11808600 57.64 16726400