Python - keep rows in dataframe based on partial string match

Question

I have 2 dataframes :
df1 is a list of mailboxes and email ids
df2 shows a list of approved domains

I read both the dataframes from an excel sheet

    xls = pd.ExcelFile(input_file_shared_mailbox)
    df = pd.read_excel(xls, sheet_name = sheet_name_shared_mailbox)

i want to only keep records in df1 where df1[Email_Id] contains df2[approved_domain]

    print(df1)  
        Mailbox Email_Id  
    0   mailbox1   abc@gmail.com  
    1   mailbox2   def@yahoo.com  
    2   mailbox3   ghi@msn.com  

    print(df2)  
        approved_domain  
    0   msn.com  
    1   gmail.com  

and i want df3 which basically shows

    print (df3)  
        Mailbox Email_Id  
    0   mailbox1   abc@gmail.com  
    1   mailbox3   ghi@msn.com  

this is the code i have right now which i think is close but i can't figure out the exact problem in the syntax

df3 = df1[df1['Email_Id'].apply(lambda x: [item for item in x if item in df2['Approved_Domains'].tolist()])]

But get this error

TypeError: unhashable type: 'list'

i spent a lot of time researching the forum for a solution but could not find what i was looking for. appreciate all the help.

Answer 1

So these are the steps you will need to follow to do what you want done for your two data frames

1.Split your email_address column into two separate columns

     df1['add'], df1['domain'] = df1['email_address'].str.split('@', 1).str

2.Then drop your add column to keep your data frame clean

      df1 = df1.drop('add',axis =1)

3.Get a new Data Frame with only values you want by not selecting any value in the 'domain' column that doesn't match 'approved_doman' column

      df_new = df1[~df1['domain'].isin(df2['approved_domain'])]

4. Drop the 'domain' column in df_new

      df_new = df_new.drop('domain',axis = 1)

This is what the result will be

    mailbox     email_address
1   mailbox2    def@yahoo.com
2   mailbox3    ghi@msn.com

Answer 2

You can use dynamically created regular expression to search for the valid domain in the list and eventually filtering them out.

Here is the code for our reference.

 # -*- coding: utf-8 -*-

import pandas as pd
import re

mailbox_list = [
        ['mailbox1', 'abc@gmail.com'],
        ['mailbox2', 'def@yahoo.com'],
        ['mailbox3', 'ghi@msn.com']]

valid_domains = ['msn.com', 'gmail.com']

df1 = pd.DataFrame(mailbox_list, columns=['Mailbox', 'EmailID'])
df2 = pd.DataFrame(valid_domains)

valid_list = []

for index, row in df1.iterrows():
    for idx, record in df2.iterrows():
        if re.search(rf"@{record[0]}", row[1], re.IGNORECASE):
            valid_list.append([row[0], row[1]])

df3 = pd.DataFrame(valid_list, columns=['Mailbox', 'EmailID'])
print(df3)

The output of this is:

    Mailbox        EmailID
0  mailbox1  abc@gmail.com
1  mailbox3    ghi@msn.com

Answer 3

Solution

df1 = {'MailBox': ['mailbox1', 'mailbox2', 'mailbox3'], 'Email_Id': ['abc@gmail.com', 'def@yahoo.com', 'ghi@msn.com']}
df2 = {'approved_domain':['msn.com', 'gmail.com']}

mailboxes, emails = zip( # unzip the columns
    *filter( # filter 
        lambda i: any([  # i = ('mailbox1', 'abc@gmail.com')
            approved_domain in i[1] for approved_domain in df2['approved_domain']
        ]),
        zip(df1['MailBox'], df1['Email_Id']) # zip the columns
    )
)

df3 = {
    'MailBox': mailboxes, 
    'Email_I': emails
}
print(df3)

Output:

> {'Email_ID': ('abc@gmail.com', 'ghi@msn.com'), 'MailBox': ('mailbox1', 'mailbox3')}                                                                                                                                                             

Some notes:

Big chunk of this code is basically just for parsing the data structure. The zipping and unzipping is only there to convert the list of columns to a list of rows and back. If you have aa list of rows already, you just have to do the filtering part