Dataframe conditional replacement with intigers

I have a dataframe column like this:

df['col_name'].unique()
>>>array([-1, 'Not Passed, On the boundary', 1, 'Passed, On the boundary',
       'Passed, Unclear result', 'Passes, Unclear result, On the boudnary',
       'Rejected, Unclear result'], dtype=object)

In this column,
if an element contains the word ‘Passed’ as a field or as a substring, then replace the entire field with integer 1 else replace it with integer -1.

Kindly help me with this

>Solution :

You can use np.where

df['col_name'] = np.where(df['col_name'].str.contains('Passed'), 1, -1)