python - In Pandas, how to delete rows from a Data Frame based on another Data Frame?

Question

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?

Answer 1

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]

Answer 2

You can also use inner join, take the indices or rows in USERS, that has email EXCLUDE, and then drop the them from the USERS. Following I use the @jezrael example to show this:

import pandas as pd
USERS = pd.DataFrame({'email': ['[email protected]',
                                '[email protected]',
                                '[email protected]',
                                '[email protected]',
                                '[email protected]']})

EXCLUDE = pd.DataFrame({'email':['[email protected]',
                                 '[email protected]']})

# rows in USERS and EXCLUDE with the same email
duplicates = pd.merge(USERS, EXCLUDE, how='inner',
                  left_on=['email'], right_on=['email'],
                  left_index=True)

# drop the indices from USERS
USERS = USERS.drop(duplicates.index)

This return:

USERS
    email
2   [email protected]
3   [email protected]
4   [email protected]

Answer 3

My solution is just to find the elements is common, extract the shared key and then use that key to remove them from the original data:

emails2remove = pd.merge(USERS, EXCLUDE, how='inner', on=['email'])['email']
USERS = USERS[ ~USERS['email'].isin(emails2remove) ]