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I would like to convert this:

          W         X         Y         Z       tdate tdim  stringdate
0  0.335737 -0.380237 -0.473996 -0.000448  2015-11-20    c  2015-11-20
1 -0.092511  1.149210 -0.834495 -0.585204  2015-05-25    c  2015-05-25
2  1.425946  1.293430 -0.545142  0.287451  2015-12-17    b  2015-12-17
3 -0.199412  1.011220  0.400355  0.470182  2015-03-21    c  2015-03-21
4  2.265234 -0.314174  0.684593 -1.360268  2015-12-23    b  2015-12-23
5 -0.192996  0.150177 -0.684978  0.464223  2015-11-19    b  2015-11-19
6 -1.089473 -1.375843 -0.901250 -0.775122  2015-07-25    c  2015-07-25
7 -0.572807 -2.484145 -2.339342 -0.263439  2015-05-31    a  2015-05-31

To this (with actual data instead of zero placeholder): 

date    2015-11-20    2015-05-25    2015-12-17    2015-03-21    2015-12-23     \
metrics          W  X          W  X          W  X          W  X          W  X   
tdim                                                                            
a                0  0          0  0          0  0          0  0          0  0   
b                0  0          0  0          0  0          0  0          0  0   
c                0  0          0  0          0  0          0  0          0  0   

date    2015-11-19    2015-07-25    2015-05-31     
metrics          W  X          W  X          W  X  
tdim                                               
a                0  0          0  0          0  0  
b                0  0          0  0          0  0  
c                0  0          0  0          0  0

I've skimmed over Pandas MultiIndexes and created one myself. That's how I arrived at the latter DataFrame, but I did not see an example in that documentation where he reindexes an existing DataFrame to have multiple column levels.

I have looked at this Pandas: add a column to a multiindex column dataframe and pandas dataframe select columns in multiindex. These are insightful but not specifically helpful for this case.

I have not attempted a brute force solution (i.e. manually populating data using loops).

I am using Python 2.7.10 and pandas 0.16.1.

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kennes
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1 Answers1

2

I'm assuming you want to use all ['W', 'X', 'Y', 'Z'] columns, and only one of the date columns. If so, the below should get you there - if you first apply set_index and then unstack, pandas creates the MultiIndex automatically, which you can then swap by level and sort as you wish:

df.columns.name = 'metrics'    
df = df.set_index(['tdate', 'tdim']).drop('stringdate', axis=1).unstack('tdate')
df = df.swaplevel(0,1,axis=1).sort_index(axis=1)

     tdate    11/19/15                                11/20/15                      \
metrics         W         X         Y         Z         W         X         Y   
tdim                                                                            
a             NaN       NaN       NaN       NaN       NaN       NaN       NaN   
b       -0.192996  0.150177 -0.684978  0.464223       NaN       NaN       NaN   
c             NaN       NaN       NaN       NaN  0.335737 -0.380237 -0.473996   

tdate             12/17/15             ...       5/25/15            5/31/15  \
metrics        Z         W        X    ...             Y        Z         W   
tdim                                   ...                                    
a            NaN       NaN      NaN    ...           NaN      NaN -0.572807   
b            NaN  1.425946  1.29343    ...           NaN      NaN       NaN   
c        -1/1/04       NaN      NaN    ...     -0.834495  -1/1/04       NaN   

tdate                                    7/25/15                                
metrics         X         Y          Z         W         X        Y          Z  
tdim                                                                            
a       -2.484145 -2.339342  -0.263439       NaN       NaN      NaN        NaN  
b             NaN       NaN        NaN       NaN       NaN      NaN        NaN  
c             NaN       NaN        NaN -1.089473 -1.375843 -0.90125  -0.775122
Stefan
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