I have 2 Data Frames, one named USERS and another named EXCLUDE. Both of them have a field named "email".
Basically, I want to remove every row in USERS that has an email contained in EXCLUDE.
How can I do it?
I'm new to Stack Overflow, and I couldn't find this question here. Any help would be very appreciated.
You can use
boolean indexing and condition with
isin, inverting boolean
Series is by
~:
import pandas as pd USERS = pd.DataFrame({'email':['a@g.com','b@g.com','b@g.com','c@g.com','d@g.com']}) print (USERS) email 0 a@g.com 1 b@g.com 2 b@g.com 3 c@g.com 4 d@g.com EXCLUDE = pd.DataFrame({'email':['a@g.com','d@g.com']}) print (EXCLUDE) email 0 a@g.com 1 d@g.com
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 b@g.com 2 b@g.com 3 c@g.com
Another solution with
merge:
df = pd.merge(USERS, EXCLUDE, how='outer', indicator=True) print (df) email _merge 0 a@g.com both 1 b@g.com left_only 2 b@g.com left_only 3 c@g.com left_only 4 d@g.com both print (df.ix[df._merge == 'left_only', ['email']]) email 1 b@g.com 2 b@g.com 3 c@g.com