pandas dataframe select columns in multiindex
PythonPandasHierarchicalMulti IndexPython Problem Overview
I have the following pd.DataFrame:
Name 0 1 ...
Col A B A B ...
0 0.409511 -0.537108 -0.355529 0.212134 ...
1 -0.332276 -1.087013 0.083684 0.529002 ...
2 1.138159 -0.327212 0.570834 2.337718 ...
It has MultiIndex columns with names=['Name', 'Col'] and hierarchical levels. The Name label goes from 0 to n, and for each label, there are two A and B columns.
I would like to subselect all the A (or B) columns of this DataFrame.
Python Solutions
Solution 1 - Python
There is a get_level_values method that you can use in conjunction with boolean indexing to get the the intended result.
In [13]:
df = pd.DataFrame(np.random.random((4,4)))
df.columns = pd.MultiIndex.from_product([[1,2],['A','B']])
print df
1 2
A B A B
0 0.543980 0.628078 0.756941 0.698824
1 0.633005 0.089604 0.198510 0.783556
2 0.662391 0.541182 0.544060 0.059381
3 0.841242 0.634603 0.815334 0.848120
In [14]:
print df.iloc[:, df.columns.get_level_values(1)=='A']
1 2
A A
0 0.543980 0.756941
1 0.633005 0.198510
2 0.662391 0.544060
3 0.841242 0.815334
Solution 2 - Python
Method 1:
df.xs('A', level='Col', axis=1)
for more refer to http://pandas.pydata.org/pandas-docs/stable/advanced.html#cross-section
Method 2:
df.loc[:, (slice(None), 'A')]
Caveat: this method requires the labels to be sorted. for more refer to http://pandas.pydata.org/pandas-docs/stable/advanced.html#the-need-for-sortedness-with-multiindex
Solution 3 - Python
EDIT* Best way now is to use indexSlice for multi-index selections
idx = pd.IndexSlice
A = df.loc[:,idx[:,'A']]
B = df.loc[:,idx[:,'B']]