Dividi intero dataframe multiIndex panda con la variabile dataframe

Question

Ho un dataframe con più indici nel modulo sottostante. Come posso dividere tutti i valori nel dataframe con df ['three']?

  one     two    three    
Number  1  2  3  1  2  3  1  2  3 
Name                 
grethe -0.299 -1.444 -0.920 1.378 0.376 -0.396 0.518 -0.816 -0.329 
hans 0.493 1.183 -0.741 -0.267 -0.564 0.281 1.550 0.544 -0.892 

Quando provo questo,

>>> df.div(df['three']) 

o questo

>>> df/df['three'] 

ottengo questo errore:

Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 218, in f 
    return self._combine_frame(other, na_op, fill_value, level) 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 3819, in _combine_frame 
    this, other = self.align(other, join='outer', level=level, copy=False) 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 2490, in align 
    fill_axis=fill_axis) 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 2521, in _align_frame 
    fill_value=fill_value) 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 2732, in _reindex_with_indexers 
    fill_value=fill_value) 
    File "C:\Anaconda\lib\site-packages\pandas\core\internals.py", line 1976, in reindex_indexer 
    return self._reindex_indexer_items(new_axis, indexer, fill_value) 
    File "C:\Anaconda\lib\site-packages\pandas\core\internals.py", line 2020, in _reindex_indexer_items 
    return BlockManager(new_blocks, new_axes) 
    File "C:\Anaconda\lib\site-packages\pandas\core\internals.py", line 1007, in __init__ 
    self._set_ref_locs(do_refs=True) 
    File "C:\Anaconda\lib\site-packages\pandas\core\internals.py", line 1117, in _set_ref_locs 
    "does not have _ref_locs set" % (block,labels)) 
AssertionError: cannot create BlockManager._ref_locs because block [FloatBlock: [1, 2, 3, one, one, one, three, three, three, two, two, two], 12 x 2, dtype float64] with duplicate items [Index([u'1', u'2', u'3', u'one', u'one', u'one', u'three', u'three', u'three', u'two', u'two', u'two'], dtype=object)] does not have _ref_locs set 

Ho anche provato impilamento come questo, senza fortuna.

>>> df.stack().div(df.stack()['three']).unstack() 

Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 220, in f 
    return self._combine_series(other, na_op, fill_value, axis, level) 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 3860, in _combine_series 
    return self._combine_match_columns(other, func, fill_value) 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 3893, in _combine_match_columns 
    left, right = self.align(other, join='outer', axis=1, copy=False) 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 2495, in align 
    fill_axis=fill_axis) 
    File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 2562, in _align_series 
    right_result = other if ridx is None else other.reindex(join_index) 
    File "C:\Anaconda\lib\site-packages\pandas\core\series.py", line 2643, in reindex 
    takeable=takeable) 
    File "C:\Anaconda\lib\site-packages\pandas\core\index.py", line 2178, in reindex 
    target = MultiIndex.from_tuples(target) 
    File "C:\Anaconda\lib\site-packages\pandas\core\index.py", line 1799, in from_tuples 
    arrays = list(lib.tuples_to_object_array(tuples).T) 
    File "inference.pyx", line 914, in pandas.lib.tuples_to_object_array (pandas\lib.c:43497) 
TypeError: Expected tuple, got str 

Answer 1

C'è una parola chiave per leveldiv/mul/add/sub che permette questo tipo di trasmissione.

In [155]: df = DataFrame(np.random.randn(2,9), 
     index=['a','b'], 
     columns=MultiIndex.from_tuples([ tuple([x,y+1]) 
      for x in ['one','two','three'] for y in range(3) ])) 

In [6]: df 
Out[6]: 
     one       two       three      
      1   2   3   1   2   3   1   2   3 
a -0.558978 -1.297585 0.150898 -1.592941 0.124235 -1.749024 1.137611 -0.389676 -1.764254 
b -1.366228 -1.192569 -1.384278 -0.970848 0.943373 0.508993 -0.451004 0.335807 -0.122192 

In [7]: df.div(df['three'],level=1) 
Out[7]: 
     one       two      three  
      1   2   3   1   2   3  1 2 3 
a -0.491362 3.329910 -0.085531 -1.400251 -0.318815 0.991367  1 1 1 
b 3.029306 -3.551347 11.328717 2.152638 2.809269 -4.165522  1 1 1