How to count rows based on multiple column conditions using pandas?

Question

How can I count csv file rows with pandas using & and or condition?

In the below code I want to count all rows that have True/False=FALSE and status = OK, and have '+' value in any of those columns openingSoon, underConstruction, comingSoon.

I've tried:

checkOne =  df['id'].loc[(df['True/False'] == 'FALSE') & (df['status'] == 'OK') & (df['comingSoon'] == '+') or (df['openingSoon'] == '+') or (df['underConstruction'] == '+')].count()

error:

Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
 File "/usr/local/lib/python3.9/site-packages/pandas/core/generic.py", line 1329, in __nonzero__
raise ValueError(
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer 1

Use | for bitwise or and for count True s values filtering not necessary, use sum :

Also for testing boolean df['True/False'] == False is possible simplify by ~df['True/False']

checkOne =  (~df['True/False'] & 
             (df['status'] == 'OK') & 
             (df['comingSoon'] == '+') | 
             (df['openingSoon'] == '+') | 
             (df['underConstruction'] == '+')).sum()

If True/False are strings TRUE/FALSE use:

checkOne =  ((df['True/False'] == 'FALSE') & 
             (df['status'] == 'OK') & 
             (df['comingSoon'] == '+') | 
             (df['openingSoon'] == '+') | 
             (df['underConstruction'] == '+')).sum()