I am working with call centre transcripts. In an ideal case the speech-to-text software will transcribe an e-mail as follows: [email protected]. This will not always be the case. So I am looking at a regular expression (RegEx) solution that accommodates white spaces in the e-mail address, e.g. maya [email protected] or maya.lucco @proton.me or maya-lucco@pro ton.me

I have unsuccessfully tried to extend this solution with regex101. Compiling a re object (pattern) as suggested in this solution seems overly complex for the task. I looked at the posts on validating e-mail addresses but they describe a different issue. Below my code so far:

import re 

#creating some data 
test = ['some random text maya @ proton.me with some more text [email protected]',
        '[email protected] with another address [email protected]',
        'some text maya.lucco @proton.me with some more bla [email protected]',
        '[email protected] more text maya@ proton.me '
        ]
        
test = pd.DataFrame(test, columns = ['words'])

#creating a function because I like to add some other data cleaning to it later on
def anonymiseEmail(text):
    
    text = str(text) #make text as string variable
    text = text.strip() @
    text = re.sub(r'S*@S*s?', '{e-mail}', text)
    
    return text

# applying the function
test['noEmail'] = test.words.apply(anonymiseEmail)

#checking the results
print(test.noEmail[0])

How can the code be extended so that the whole e-mail address, regardless of how many white spaces it has, be replaced with a placed holder or deleted?