Pandas: Multilevel column names

Question

pandas has support for multi-level column names:

>>>  x = pd.DataFrame({'instance':['first','first','first'],'foo':['a','b','c'],'bar':rand(3)}) >>> x = x.set_index(['instance','foo']).transpose() >>> x.columns MultiIndex [(u'first', u'a'), (u'first', u'b'), (u'first', u'c')] >>> x instance     first                     foo              a         b         c bar       0.102885  0.937838  0.907467 

This feature is very useful since it allows multiple versions of the same dataframe to be appended 'horizontally' with the 1st level of the column names (in my example instance) distinguishing the instances.

Imagine I already have a dataframe like this:

                 a         b         c bar       0.102885  0.937838  0.907467 

Is there a nice way to add another level to the column names, similar to this for row index:

x['instance'] = 'first' x.set_level('instance',append=True) 

Answer 1

Try this:

df=pd.DataFrame({'a':[1,2,3],'b':[4,5,6]})  columns=[('c','a'),('c','b')]  df.columns=pd.MultiIndex.from_tuples(columns) 

Answer 2

You can use concat. Give it a dictionary of dataframes where the key is the new column level you want to add.

In [46]: d = {}  In [47]: d['first_level'] = pd.DataFrame(columns=['idx', 'a', 'b', 'c'],                                          data=[[10, 0.89, 0.98, 0.31],                                                [20, 0.34, 0.78, 0.34]]).set_index('idx')  In [48]: pd.concat(d, axis=1) Out[48]:     first_level               a     b     c idx 10         0.89  0.98  0.31 20         0.34  0.78  0.34 

You can use the same technique to create multiple levels.

In [49]: d['second_level'] = pd.DataFrame(columns=['idx', 'a', 'b', 'c'],                                           data=[[10, 0.29, 0.63, 0.99],                                                 [20, 0.23, 0.26, 0.98]]).set_index('idx')  In [50]: pd.concat(d, axis=1) Out[50]:     first_level             second_level               a     b     c            a     b     c idx 10         0.89  0.98  0.31         0.29  0.63  0.99 20         0.34  0.78  0.34         0.23  0.26  0.98