Python script to highlight data mismatch from predefined list

I have a predefined list of Service and CI in Excel:

I get an extract of data from our call logging system which also has the Service and CI columns.
I need to highlight if the Service and CI in the call extract does not match the predefined list.

My code so far works for one Service in the predefined list, I need to figure out how to add the rest of the listed Services in my predefined list. IF I run it as is it works for the Service named Non-Financial Risk Management South Africa but highlights all the other Services in RED.

from pathlib import Path
import pandas as pd
import numpy as np
import os

extract = Path.cwd() / "/extract.xlsx"
df_extract = pd.read_excel(extract)


m = (df_extract['Service'] == 'Non-Financial Risk Management South Africa') & (df_extract['CI'].isin(['Aravo', 'Business Resilience', 'Change Risk Management']))


(df_extract.style.apply(lambda x: np.where(m, '', 'background-color: red'))
   .to_excel('/output.xlsx', index=False))

I tried adding a 2nd boolean mask but I cant figure out how to integrate with the np.where:

n = (df_extract['Service'] == 'First Line Control Attestation South Africa') & (df_extract['CI'].isin(['Control First']))

>Solution :

I think you need chain both masks by | for bitwise OR:

(df_extract.style.apply(lambda x: np.where(m | n, '', 'background-color: red'))
           .to_excel('/output.xlsx', index=False))

If there is more masks is possible create dictionary and pass to mask for more readable code:

#add more key:values if necessary
d = {'Non-Financial Risk Management South Africa':['Aravo', 'Business Resilience',
                                                   'Change Risk Management'],
     'First Line Control Attestation South Africa':['Control First']}

mask = np.logical_or.reduce([df_extract['Service'].eq(k) &  df_extract['CI'].isin(v)  
                             for k, v in d.items()])

(df_extract.style.apply(lambda x: np.where(mask, '', 'background-color: red'))
           .to_excel('/output.xlsx', index=False))