pandas: bypassing numerical index
Sometimes I am dealing with DataFrames that have a numerical index, but I would like to bypass it to reference rows according to their order,

    In [49]: df = pandas.DataFrame(np.random.randn(3, 5))
    
    In [50]: df
    Out[50]: 
              0         1         2         3         4
    0 -2.426211  0.670384  0.545880 -1.435168  0.675598
    1  0.507128  0.478832 -0.159536 -0.696284 -1.112171
    2  0.938019 -1.673491 -0.567462  0.381804 -1.280602
    
    In [51]: df[1:2]
    Out[51]: 
              0         1         2         3         4
    1  0.507128  0.478832 -0.159536 -0.696284 -1.112171
    
    In [52]: df2 = df.ix[1:2]
    
    In [53]: df2
    Out[53]: 
              0         1         2         3         4
    1  0.507128  0.478832 -0.159536 -0.696284 -1.112171
    2  0.938019 -1.673491 -0.567462  0.381804 -1.280602
    
    In [55]: df2.ix[0]
    ---------------------------------------------------------------------------
    KeyError                                  Traceback (most recent call last)
    ...

    In [56]: df2.rename(index={1: "one", 2:"two"})
    Out[56]: 
                0         1         2         3         4
    one  0.507128  0.478832 -0.159536 -0.696284 -1.112171
    two  0.938019 -1.673491 -0.567462  0.381804 -1.280602
    
    In [57]: df3 = df2.rename(index={1: "one", 2:"two"})
    
    In [58]: df3.ix[0]
    Out[58]: 
    0    0.507128
    1    0.478832
    2   -0.159536
    3   -0.696284
    4   -1.112171
    Name: one

How can I bypass pandas' indices to access the underlying matrix indices?
pandas: bypassing numerical index
===

Sometimes I am dealing with DataFrames that have a numerical index, but I would like to bypass it to reference rows according to their order,

    In [49]: df = pandas.DataFrame(np.random.randn(3, 5))
    
    In [50]: df
    Out[50]: 
              0         1         2         3         4
    0 -2.426211  0.670384  0.545880 -1.435168  0.675598
    1  0.507128  0.478832 -0.159536 -0.696284 -1.112171
    2  0.938019 -1.673491 -0.567462  0.381804 -1.280602
    
    In [51]: df[1:2]
    Out[51]: 
              0         1         2         3         4
    1  0.507128  0.478832 -0.159536 -0.696284 -1.112171
    
    In [52]: df2 = df.ix[1:2]
    
    In [53]: df2
    Out[53]: 
              0         1         2         3         4
    1  0.507128  0.478832 -0.159536 -0.696284 -1.112171
    2  0.938019 -1.673491 -0.567462  0.381804 -1.280602
    
    In [55]: df2.ix[0]
    ---------------------------------------------------------------------------
    KeyError                                  Traceback (most recent call last)
    ...

    In [56]: df2.rename(index={1: "one", 2:"two"})
    Out[56]: 
                0         1         2         3         4
    one  0.507128  0.478832 -0.159536 -0.696284 -1.112171
    two  0.938019 -1.673491 -0.567462  0.381804 -1.280602
    
    In [57]: df3 = df2.rename(index={1: "one", 2:"two"})
    
    In [58]: df3.ix[0]
    Out[58]: 
    0    0.507128
    1    0.478832
    2   -0.159536
    3   -0.696284
    4   -1.112171
    Name: one

How can I bypass pandas' indices to access the underlying matrix indices?