Email Validation using Regular Expressions Pandas Dataframe

Question

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': ['[email protected]','[email protected]','[email protected]','[email protected]','']}
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.

fsimonjetz · Accepted Answer · 2022-08-23 19:34:51Z

2

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,}

edited Aug 23, 2022 at 19:34

answered Aug 23, 2022 at 19:32

fsimonjetz

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2 Comments

Onyambu Over a year ago

thie description is an oversimplification. This will match something like @@@.@@ which is not a valid emai address

fsimonjetz Over a year ago

@onyambu yes, you're absolutely right. The crux is str.match though, and I think OP would be better off using some ready-made solution for a common task like e-mail recognition, anyway. (If it is crucial to the task, that is, and the question didn't sound like a precise matcher is needed).

Jeff Martin · Accepted Answer · 2022-08-23 19:30:59Z

1

The beautiful thing about Pandas dataframes is that you almost never have to loop through them--and avoiding loops will increase your speed significantly.

df['Email Address'].str.contains(regex) will return a boolean Series of whether each observation in the Email Address column.

Check out this chapter on vectorized string operations for more.

answered Aug 23, 2022 at 19:30

Jeff Martin

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Comments

Dylan Ko · Accepted Answer · 2022-08-23 19:29:34Z

0

You can iterate through rows using .iterrows() on a dataframe. row contains a series and you can access your column the same way you would a dictionary.

for i, row in df.iterrows():
    if not isValid(row['Email Address']):
        print("Invalid email")

answered Aug 23, 2022 at 19:29

Dylan Ko

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1 Comment

Dylan Ko Over a year ago

The function defined in the question.

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