I want to check if on any dataframe row a given number of columns has any of a set of values (different sets for different columns) and assign a boolean accordingly - I think I might need a combination of apply() and any() but not quite hitting it exactly: 
So, for dataframe:
bank_dict = {'Name' : ['A', 'B', 'C', 'D', 'E'],
        'Type' :     ['Retail', 'Corporate', 'Corporate', 'Wholesale', 'Retail'],
        'Overdraft': ['Y', 'Y', 'Y', 'N', 'N'],
        'Forex': ['USD', 'GBP', 'EUR', 'JPY', 'GBP']}
With truth list:
truth_list = [bank_df['Type'].isin(['Retail']), bank_df['Overdraft'].isin(['Yes']), bank_df['Forex'].isin(['USD', 'GBP'])]
The resultant df should look like:
  Name       Type Overdraft Forex  TruthCol
0    A     Retail         Y   USD         1
1    B  Corporate         Y   GBP         1
2    C  Corporate         Y   EUR         1
3    D  Wholesale         N   JPY         0
4    E     Retail         N   GBP         1
Thanks,
 
     
     
    