Email Validation using Regular Expressions Pandas Dataframe-CodePudding

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.

CodePudding user response：

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,}

CodePudding user response：

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.

CodePudding user response：

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")