You can use boolean indexing and condition with isin, inverting boolean Series is by ~:
import pandas as pd
USERS = pd.DataFrame({'email':['[email protected]','[email protected]','[email protected]','[email protected]','[email protected]']})
print (USERS)
email
0 [email protected]
1 [email protected]
2 [email protected]
3 [email protected]
4 [email protected]
EXCLUDE = pd.DataFrame({'email':['[email protected]','[email protected]']})
print (EXCLUDE)
email
0 [email protected]
1 [email protected]
print (USERS.email.isin(EXCLUDE.email))
0 True
1 False
2 False
3 False
4 True
Name: email, dtype: bool
print (~USERS.email.isin(EXCLUDE.email))
0 False
1 True
2 True
3 True
4 False
Name: email, dtype: bool
print (USERS[~USERS.email.isin(EXCLUDE.email)])
email
1 [email protected]
2 [email protected]
3 [email protected]
Another solution with merge:
df = pd.merge(USERS, EXCLUDE, how='outer', indicator=True)
print (df)
email _merge
0 [email protected] both
1 [email protected] left_only
2 [email protected] left_only
3 [email protected] left_only
4 [email protected] both
print (df.loc[df._merge == 'left_only', ['email']])
email
1 [email protected]
2 [email protected]
3 [email protected]