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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.

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4 Answers 4

Reset to default
7

Try this:

df = df[~df.filter(like='user').apply(set, axis=1).eq({'pass'})]
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3 
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)

enter image description here

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

enter image description here

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

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1 
df = df.drop((df.loc[(df['user1'] == 'pass') && df['user2'] == 'pass']).index)
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0

You can slice the DataFrame with conditionals.

df[(df['user1'] != 'pass') & (df['user2'] != 'pass')]

If you want to make this the new df just use:

df = df[(df['user1'] != 'pass') & (df['user2'] != 'pass')]
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1 Comment

Sorry, forgot to mention: the names and amount of user columns could increase :) e.g. there could be user1, user2, user3 and so on

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