Given a DataFrame that contains multiple columns (possible regressors), how can I generate all possible combinations of columns to test them into different regressions? I'm trying to select the best regression model from all the possible combination of regressors.
For example, I have this DataFrame
:
A B
1/1/2011 1 4
1/2/2011 2 5
1/3/2011 3 6
and I want to generate the following ones:
A B
1/1/2011 1 4
1/2/2011 2 5
1/3/2011 3 6
A
1/1/2011 1
1/2/2011 2
1/3/2011 3
B
1/1/2011 4
1/2/2011 5
1/3/2011 6
If you are looking for combination of columns to regression against each other
df = DataFrame(numpy.random.randn(3,6), columns=['a','b','c','d','e','g'])
df2 =[df[list(pair)] for pair in list(iter.combinations(df.columns, 2))]
Try using itertools to generate the powerset of column names:
In [23]: import itertools as iter
In [24]: def pset(lst):
....: comb = (iter.combinations(lst, l) for l in range(len(lst) + 1))
....: return list(iter.chain.from_iterable(comb))
....:
In [25]: pset(lst)
Out[25]:
[(),
('A',),
('B',),
('C',),
('D',),
('A', 'B'),
('A', 'C'),
('A', 'D'),
('B', 'C'),
('B', 'D'),
('C', 'D'),
('A', 'B', 'C'),
('A', 'B', 'D'),
('A', 'C', 'D'),
('B', 'C', 'D'),
('A', 'B', 'C', 'D')]
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