Email Validation using Regular Expressions Pandas Dataframe

I would like to do a simple email validation for list import of email addresses into a database. I just want to make sure that there is content before the @ sign, an @ sign, content after the @ sign, and 2+ characters after the ‘.’ . Here is a sample df:

import pandas as pd
import re

errors= {}

data= {'First Name': ['Sally', 'Bob', 'Sue', 'Tom', 'Will'],
     'Last Name': ['William', '', 'Wright', 'Smith','Thomas'],
     'Email Address': ['sally@gmail.co.uk','bobby123@gmail.com','suewright_123@yahoo.gov','tom.smith23@students.wacs.fl.us','']}
df=pd.DataFrame(data)

This is the expression I was using to check for valid emails:

regex = re.compile(r'([A-Za-z0-9]+[.-_])*[A-Za-z0-9]+@[A-Za-z0-9-]+(\.[A-Z|a-z]{2,})+')
def isValid(email):
    if re.fullmatch(regex, email):
      pass
    else:
      return("Invalid email")

This regex is working fine but I am not sure how to easily loop through my entire df email address column. I have tried:

for col in df['Email Address'].columns:
   for i in df['Email Address'].index:
      if df.loc[i,col] = 'Invalid email'
           errors={'row':i, 'column':col, 'message': 'this is not a valid email address'

I am wanting to write the invalid email to a dictionary titled errors. with the above code I get an invalid error.

>Solution :

According to your description, I’d probably do

df["Email Address"].str.match(r"^.+@.+\..{2,}$")

str.match returns True if the regex matches the string.

The regex is

• the start of the string ^
• content before the @ sign .+
• an @ sign @
• content after the @ sign .+
• a dot \.
• and 2+ characters after the ‘.’ .{2,}