Cross sections of different axes with MultiIndex

Selection of cross-sections using .xs

In [1]:
import pandas as pd
import numpy as np
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
idx_row = pd.MultiIndex.from_arrays(arrays, names=['Row_First', 'Row_Second'])
idx_col = pd.MultiIndex.from_product([['A','B'], ['i', 'ii']], names=['Col_First','Col_Second'])
df = pd.DataFrame(np.random.randn(8,4), index=idx_row, columns=idx_col)

Out[1]:
Col_First                    A                   B          
Col_Second                   i        ii         i        ii
Row_First Row_Second                                        
bar       one        -0.452982 -1.872641  0.248450 -0.319433
          two        -0.460388 -0.136089 -0.408048  0.998774
baz       one         0.358206 -0.319344 -2.052081 -0.424957
          two        -0.823811 -0.302336  1.158968  0.272881
foo       one        -0.098048 -0.799666  0.969043 -0.595635
          two        -0.358485  0.412011 -0.667167  1.010457
qux       one         1.176911  1.578676  0.350719  0.093351
          two         0.241956  1.082138 -0.516898 -0.196605

.xs accepts a level (either the name of said level or an integer), and an axis: 0 for rows, 1 for columns.

.xs is available for both pandas.Series and pandas.DataFrame.

Selection on rows:

In [2]: df.xs('two', level='Row_Second', axis=0)
Out[2]:  
Col_First          A                   B          
Col_Second         i        ii         i        ii
Row_First                                         
bar        -0.460388 -0.136089 -0.408048  0.998774
baz        -0.823811 -0.302336  1.158968  0.272881
foo        -0.358485  0.412011 -0.667167  1.010457
qux         0.241956  1.082138 -0.516898 -0.196605

Selection on columns:

In [3]: df.xs('ii', level=1, axis=1)
Out[3]:
Col_First                    A         B
Row_First Row_Second                    
bar       one        -1.872641 -0.319433
          two        -0.136089  0.998774
baz       one        -0.319344 -0.424957
          two        -0.302336  0.272881
foo       one        -0.799666 -0.595635
          two         0.412011  1.010457
qux       one         1.578676  0.093351
          two         1.082138 -0.196605

.xs only works for selection , assignment is NOT possible (getting, not setting):¨

In [4]: df.xs('ii', level='Col_Second', axis=1) = 0
  File "<ipython-input-10-92e0785187ba>", line 1
    df.xs('ii', level='Col_Second', axis=1) = 0
                                               ^
SyntaxError: can't assign to function call

Using .loc and slicers

Unlike the .xs method, this allows you to assign values. Indexing using slicers is available since version 0.14.0.

In [1]:
import pandas as pd
import numpy as np
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
idx_row = pd.MultiIndex.from_arrays(arrays, names=['Row_First', 'Row_Second'])
idx_col = pd.MultiIndex.from_product([['A','B'], ['i', 'ii']], names=['Col_First','Col_Second'])
df = pd.DataFrame(np.random.randn(8,4), index=idx_row, columns=idx_col)

Out[1]:
Col_First                    A                   B          
Col_Second                   i        ii         i        ii
Row_First Row_Second                                        
bar       one        -0.452982 -1.872641  0.248450 -0.319433
          two        -0.460388 -0.136089 -0.408048  0.998774
baz       one         0.358206 -0.319344 -2.052081 -0.424957
          two        -0.823811 -0.302336  1.158968  0.272881
foo       one        -0.098048 -0.799666  0.969043 -0.595635
          two        -0.358485  0.412011 -0.667167  1.010457
qux       one         1.176911  1.578676  0.350719  0.093351
          two         0.241956  1.082138 -0.516898 -0.196605

Selection on rows:

In [2]: df.loc[(slice(None),'two'),:]
Out[2]: 
Col_First                    A                   B          
Col_Second                   i        ii         i        ii
Row_First Row_Second                                        
bar       two        -0.460388 -0.136089 -0.408048  0.998774
baz       two        -0.823811 -0.302336  1.158968  0.272881
foo       two        -0.358485  0.412011 -0.667167  1.010457
qux       two         0.241956  1.082138 -0.516898 -0.196605

Selection on columns:

In [3]: df.loc[:,(slice(None),'ii')]
Out[3]: 
Col_First                    A         B
Col_Second                  ii        ii
Row_First Row_Second                    
bar       one        -1.872641 -0.319433
          two        -0.136089  0.998774
baz       one        -0.319344 -0.424957
          two        -0.302336  0.272881
foo       one        -0.799666 -0.595635
          two         0.412011  1.010457
qux       one         1.578676  0.093351
          two         1.082138 -0.196605

Selection on both axis::

In [4]: df.loc[(slice(None),'two'),(slice(None),'ii')]
Out[4]: 
Col_First                    A         B
Col_Second                  ii        ii
Row_First Row_Second                    
bar       two        -0.136089  0.998774
baz       two        -0.302336  0.272881
foo       two         0.412011  1.010457
qux       two         1.082138 -0.196605

Assignment works (unlike .xs):

In [5]: df.loc[(slice(None),'two'),(slice(None),'ii')]=0
         df
Out[5]: 
Col_First                    A                   B          
Col_Second                   i        ii         i        ii
Row_First Row_Second                                        
bar       one        -0.452982 -1.872641  0.248450 -0.319433
          two        -0.460388  0.000000 -0.408048  0.000000
baz       one         0.358206 -0.319344 -2.052081 -0.424957
          two        -0.823811  0.000000  1.158968  0.000000
foo       one        -0.098048 -0.799666  0.969043 -0.595635
          two        -0.358485  0.000000 -0.667167  0.000000
qux       one         1.176911  1.578676  0.350719  0.093351
          two         0.241956  0.000000 -0.516898  0.000000