Найдите строки, в которых значения в столбцах не соответствуют значениям из списка Python

Одна идея:

#list down all the variations of accepted email domains
email_adds = ['@abc.com','@ABC.COM','@Abc.com']
#combine the variations of email addresses in the list
accepted_emails = '|'.join(email_adds)

#columns for test
c = ['sup1_email','sup2_email', 'sup3_email', 'sup4_email']
#reshape and test all values, if `nan` pass `True`
m = df[c].stack(dropna=False).str.contains(accepted_emails, na=True).unstack().all(axis=1)

df = df[~m]
print (df)
   personid      sup1_email         sup2_email        sup3_email  \
4        65  [email protected]  [email protected]    [email protected]   
5        89   [email protected]   [email protected]  [email protected]   

           sup4_email  
4     [email protected]  
5  [email protected]  

Ваше решение с генератором и any:

c = ['sup1_email','sup2_email', 'sup3_email', 'sup4_email']

f = lambda y: any(x in y for x in email_adds) if isinstance(y, str) else True
df = df[~df[c].applymap(f).all(axis=1)]
print (df)
   personid      sup1_email         sup2_email        sup3_email  \
4        65  [email protected]  [email protected]    [email protected]   
5        89   [email protected]   [email protected]  [email protected]   

           sup4_email  
4     [email protected]  
5  [email protected]  

Вы можете сделать:

# list down all the variations of accepted email domains
email_adds = ['@abc.com','@ABC.COM','@Abc.com']

# combine the variations of email addresses in the list
accepted_emails = '|'.join(email_adds)

# create a single email column
melted = df.melt('personid')

# check the matching emails
mask = melted['value'].str.contains(accepted_emails, na=True)

# filter out the ones that do not match
mask = df['personid'].isin(melted.loc[~mask, 'personid'])

print(df[mask])

Выход

   personid      sup1_email  ...        sup3_email          sup4_email
4        65  [email protected]  ...    [email protected]     [email protected]
5        89   [email protected]  ...  [email protected]  [email protected]

[2 rows x 5 columns]

Давайте попробуем проверить, чтобы символы после @ были либо ABC, либо abc, либо Abc во всех столбцах. Конечно, нам может понадобиться временно отфильтровать PersonID. После проверки отмените результат с помощью ~ и замаскируйте их.

df[-(df.iloc[:,1:].apply(lambda x: x.str.contains('(\@(?=ABC|abc|Abc))').all(), axis=1))]



 personid      sup1_email         sup2_email        sup3_email  \
4      65.0  [email protected]  [email protected]    [email protected]   
5      89.0   [email protected]   [email protected]  [email protected]   

           sup4_email  
4     [email protected]  
5  [email protected]