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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@…How to Validate an Email Address and Raise ValueError in Python
This code defines a validate_email function that checks an email address against a regex pattern and several rules, raising ValueError with a specific reason when invalid.
import re
def validate_email(email: str) -> str:
"""Validate an email address and return it if valid, otherwise raise ValueError."""
if not isinstance(email, str):
raise ValueError("Email must be a string")
if len(email) > 254:
raise ValueError("Email length exceeds 254 characters")
#…How to Validate User Input with a Dataclass in Python
A dataclass stores name, age, and email, and a validator class checks each field, returning a dictionary of boolean results.
from dataclasses import dataclass
@dataclass
class UserInput:
name: str
age: int
email: str
def is_valid_name(self) -> bool:
return bool(self.name.strip()) and len(self.name.strip()) >= 2
def is_valid_age(self) -> bool:
return isinstance(self.age, int) and 0 < self.age < 150
…How to Redact Emails and Phones Before Sending to an LLM in Python
This code uses regular expressions to replace email addresses and US phone numbers with [EMAIL] and [PHONE] placeholders before any LLM processing.
import re
def redact_pii(text: str) -> str:
# Replace email addresses with [EMAIL]
text = re.sub(r'[\w.+-]+@[\w-]+\.[\w.-]+', '[EMAIL]', text)
# Replace phone numbers (US format) with [PHONE]
text = re.sub(r'\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}', '[PHONE]', text)
return text
if __name__ == "__main…Generate Random Fake User Data for Testing in Python
This code generates a list of fake user dictionaries with random names, emails, ages, and timestamps using the Python standard library for testing purposes.
import json
import random
import string
from datetime import datetime, timedelta
def generate_user_data(num_users=1):
first_names = ["Alice", "Bob", "Charlie", "Diana", "Eve"]
last_names = ["Smith", "Johnson", "Brown", "Taylor", "Wilson"]
domains = ["example.com", "test.org", "demo.net"]
users = …Run pytest and email summary in Python
Runs pytest via subprocess, extracts the test summary line, and sends it in an email (mocked for demonstration).
import smtplib
import subprocess
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
def run_tests():
"""Run pytest and capture the summary output."""
result = subprocess.run(
["pytest", "-q"],
capture_output=True,
text=True
)
return result.stdo…How to Hash Email Addresses in a PII Masking Pipeline in Python
Replaces every email address in a text string with its SHA-256 hash to protect personally identifiable information (PII).
import hashlib
import re
def hash_email(email: str) -> str:
"""Mask an email address by hashing it with SHA-256."""
normalized = email.strip().lower()
return hashlib.sha256(normalized.encode("utf-8")).hexdigest()
def mask_pii_emails(text: str) -> str:
"""Replace all email addresses in text with their…How to Parse Data in Python: A Beginner's Helper
This helper parses a JSON payload, extracts user names, emails, and signup dates, then summarizes the results.
import json
from datetime import datetime
from typing import Dict, List
def parse_data(payload: str) -> Dict[str, List]:
"""Parse a JSON payload and extract useful fields."""
raw = json.loads(payload)
users = raw.get("users", [])
parsed = {
"names": [],
"emails": [],
"signup_…How to Validate Data in a Python Pipeline
A helper module to validate common record types — email, positive integer, and non-empty string list — before processing data in a pipeline.
from typing import Any, Iterable
def is_valid_email(email: str) -> bool:
"""Basic email check: one '@', no spaces, dot after '@'."""
if "@" not in email or " " in email:
return False
local, _, domain = email.partition("@")
return bool(local) and "." in domain
def is_positive_int(value: Any)…Generate Fake User Data with Faker in Python
Use the Faker library to generate realistic fake user profiles with names, emails, phone numbers, and addresses for tests or demos.
from faker import Faker
fake = Faker()
def generate_user():
return {
"name": fake.name(),
"email": fake.email(),
"phone": fake.phone_number(),
"address": fake.address().replace("\n", ", "),
}
if __name__ == "__main__":
user = generate_user()
for key, value in user.ite…How to Mock Hexagonal Architecture Ports and Adapters in Python
Mock an email adapter in a hexagonal architecture with unittest.mock to test business logic in isolation.
from unittest.mock import Mock
class EmailService:
def send(self, recipient, message):
raise NotImplementedError
class OrderProcessor:
def __init__(self, email_service):
self.email_service = email_service
def process_order(self, order_id, customer_email):
# Business logic
…How to Route Alerts by Severity in Python
Map alert severity levels to routing targets and simulate dispatching alerts to on-call pages, email, Slack, or logs.
def main():
# Severity levels with corresponding alert routing targets
routing_map = {
"critical": "call_page",
"high": "call_page",
"medium": "email_team",
"low": "slack_channel",
"info": "log_only"
}
# Simulated alerts with severity
alerts = [
{"na…Browse by section
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