Strings & text
Format, split, join, parse, and clean text — everyday Python string patterns.
Extract Email-Like Tokens from Text in Python
Uses a regular expression to find all email-like tokens in a string, returning them as a list with re.findall.
import re
def extract_email_like_tokens(text):
pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b'
return re.findall(pattern, text)
if __name__ == "__main__":
sample_text = (
"Contact us at support@example.com or sales@company.co.uk. "
"Invalid: hello@world, user@.com, test@do…How to Detect PII in Documents Using Python
Use regex patterns to automatically detect emails, phone numbers, SSNs, and credit card numbers in text documents.
import re
from typing import List, Dict
def detect_pii(text: str) -> Dict[str, List[str]]:
patterns = {
"email": r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}",
"phone": r"\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}",
"ssn": r"\b\d{3}-\d{2}-\d{4}\b",
"credit_card": r"\b\d{4}[- ]?\d{4}[-…Validate email format with regex in Python
A Python function using a regex pattern to validate simple email formats, returning True or False for each input.
import re
def is_valid_email(email):
pattern = r'^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$'
return bool(re.match(pattern, email))
if __name__ == "__main__":
test_emails = [
"user@example.com",
"first.last@sub.domain.org",
"invalid-email",
"user@.com",
"user@…Browse by section
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Strings & text — Python code examples
What you will find here
This page collects strings & text snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.