combining groups of columns with boolean values to create multiple new columns
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Chapters
00:00 Question
00:58 Accepted answer (Score 3)
01:31 Thank you
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Full question
https://stackoverflow.com/questions/6357...
Accepted answer links:
[itertools.product]: https://docs.python.org/3/library/iterto...
[Series.mul]: https://pandas.pydata.org/pandas-docs/st...
[pd.concat]: https://pandas.pydata.org/pandas-docs/st...
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Content licensed under CC BY-SA
https://meta.stackexchange.com/help/lice...
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Tags
#python #pandas #numpy #dataframe #listcomprehension
#avk47
--
Music by Eric Matyas
https://www.soundimage.org
Track title: Future Grid Looping
--
Chapters
00:00 Question
00:58 Accepted answer (Score 3)
01:31 Thank you
--
Full question
https://stackoverflow.com/questions/6357...
Accepted answer links:
[itertools.product]: https://docs.python.org/3/library/iterto...
[Series.mul]: https://pandas.pydata.org/pandas-docs/st...
[pd.concat]: https://pandas.pydata.org/pandas-docs/st...
--
Content licensed under CC BY-SA
https://meta.stackexchange.com/help/lice...
--
Tags
#python #pandas #numpy #dataframe #listcomprehension
#avk47
ACCEPTED ANSWER
Score 3
Use itertools.product to get the cartesian product of column names then use Series.mul inside a list comprehension to create corresponding column products, finally use pd.concat to concat these products with df:
from itertools import product
l1, l2 = ['A', 'B'], ['X', 'Y']
c = [df[a].mul(df[b]).rename(''.join([a, b])) for a, b in product(l1, l2)]
df = pd.concat([df] + c, axis=1)
Result:
A B X Y AX AY BX BY
0 True False True False True False False False