Remove a row in pandas dataframe if multiple columns contains specific value

Question

I've tried looking for an answer to this question but haven't found anything that works for me. I want to remove rows in which certain columns contain specific values. In my case, I want to remove rows where each column that starts with "user" in that row contains the value "pass". E.g. if I have the following dataframe:

idx   start   end  user1   user2 
0     1       3    pass     pass   
1     4       5   burger   burger 
2     6       7   nuggets    cola 
3     10     15     pass    burger

I'd like to remove row 0, but not row 3 as only one of the columns contains the value "pass". Would appreciate any help!

Update: The names and amount of user columns could increase, e.g. there could be user1, user2, user3 and so on.

Answer 1

Try this:

df = df[~df.filter(like='user').apply(set, axis=1).eq({'pass'})]

Answer 2

x = np.array([[0,1,2], [3,2,5], [6,2,9], ['pass', 'pass', 1], [1, 2, 'pass']])

df = pd.DataFrame(x, columns=['one', 'two', 'three'])
display(df)

df[(df['one'] != 'pass') & (df['two'] != 'pass')]

as you can see you can pass multiple conditions for your indexation in Pandas.