Python: Adding a Dataframe column based on three other columns, two being filled with True/False

Question

This is a mock dataframe of a much larger dataframe. I have this:

ID	Category	Shipped T/F?	Hold T/F?
123	ABC	False	False
456	ABC	False	True
789	ABC	True	False
234	ABC	True	True
567	DEF	False	False
678	DEF	False	False
135	DEF	False	False
246	DEF	False	False
369	KGH	True	True

I want this:

ID	Category	Shipped T/F?	Hold T/F?	NewCol
123	ABC	False	False	1
456	ABC	False	True	1
789	ABC	True	False	1
234	ABC	True	True	0
567	DEF	False	False	0
678	DEF	False	False	0
135	DEF	False	False	0
246	DEF	False	False	0
369	KGH	True	True	0

My code:

import pandas as pd
import numpy as np

data=[
    ['123','ABC', False, False],
    ['456','ABC', False, True],
    ['789','ABC', True, False],
    ['234','ABC', True, True],
    ['567','DEF', False, False],
    ['678','DEF', False, True],
    ['135','DEF', True, False],
    ['246','DEF', True, True],
    ['369','KGH', True, True]
]
df = pd.DataFrame(data,columns=['ID','Category','Shipped T/F?','Hold T/F?'])

Conditions = [
    (df['Category'] != 'ABC'),
    (df['Shipped T/F?'].bool==True and df['Hold T/F?'].bool==True)
    (df['Category'] == 'ABC')
]
values=[0,0,1]
df['NewCol'] = np.select(Conditions,values)

print(df)

This is the error I'm getting: Exception has occurred: TypeError 'bool' object is not callable File "C:\Users\Johnr50\Documents\repos\SLHRoutine\testTFLkup.py", line 19, in (df['Shipped T/F?'].bool==True and df['Hold T/F?'].bool==True) TypeError: 'bool' object is not callable

I have tried the following solutions: https://www.learndatasci.com/solutions/python-valueerror-truth-value-series-ambiguous-use-empty-bool-item-any-or-all/ https://www.dataquest.io/blog/tutorial-add-column-pandas-dataframe-based-on-if-else-condition/ And several others form StackOverflow which I can no longer find since I am posting this question. Update a df column based on three other columns values using a function Create New True/False Pandas Dataframe Column based on conditions

`(df['Shipped T/F?'] & df['Hold T/F?']),`? – BigBen Feb 22 '23 at 15:13 — BigBen, Feb 22 '23 at 15:13

score 1 · Accepted Answer · answered Feb 22 '23 at 15:16

Modify Conditions:

Conditions = [
    df['Category'] != 'ABC',
    df['Shipped T/F?'] & df['Hold T/F?'],  # <- HERE
    df['Category'] == 'ABC'
]

Output:

>>> out
    ID Category  Shipped T/F?  Hold T/F?  NewCol
0  123      ABC         False      False       1
1  456      ABC         False       True       1
2  789      ABC          True      False       1
3  234      ABC          True       True       0
4  567      DEF         False      False       0
5  678      DEF         False       True       0
6  135      DEF          True      False       0
7  246      DEF          True       True       0
8  369      KGH          True       True       0

Thank you so much! This worked. I did not expect it the T/F comparison to so closely mirror the way Excel works. — RhettBrindle, Feb 23 '23 at 02:27

Python: Adding a Dataframe column based on three other columns, two being filled with True/False

1 Answers1