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I want to create a new column in my pandas dataframe based on values in already existing columns. The input of the new column should be boolean. At the moment I am trying the following:

import pandas as pd df_edit = pd.DataFrame({'Included': [False, False, True, False], 'Update Check': [True, True, True, True], 'duplicate_fname': [True, False, False, False], 'duplicate_targetfname': [False, False, False, False]}) df_edit['test'] = df_edit['Included'] == False & df_edit['Update Check'] == True & (df_edit['duplicate_fname'] == True | df_edit['duplicate_targetfname'] == True)

When I try to do it like this I get a ValueError stating the following:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), >a.item(), a.any() or a.all().

Is there another way to do this?

My expected output would be a column that consists of the following values: 

True, False, False, False
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    They have to be enclosed in braces. Commented May 30, 2018 at 8:05

1 Answer 1

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There is problem not enclosing parentheses:

df_edit['test'] = (df_edit['Included'] == False) & \ (df_edit['Update Check'] == True) & \ ((df_edit['duplicate_fname'] == True) | (df_edit['duplicate_targetfname'] == True)) print (df_edit) Included Update Check duplicate_fname duplicate_targetfname test 0 False True True False True 1 False True False False False 2 True True False False False 3 False True False False False

But better is use ~ for invert boolean mask and omit compare with Trues:

df_edit['test'] = ~df_edit['Included'] & df_edit['Update Check'] & (df_edit['duplicate_fname'] | df_edit['duplicate_targetfname']) print (df_edit) Included Update Check duplicate_fname duplicate_targetfname test 0 False True True False True 1 False True False False False 2 True True False False False 3 False True False False False
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2 Comments

Wow, that was fast. Thank you very much!
@LaurensdeWit - I obviously remove answer because dupe, but becauxe here is better solution keep it.

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