fm-dlp 3.6.2

CLI tool for searching YouTube/YTMusic and downloading audio/video from 1000+ platforms

re-kiss — A powerful set of tools for working with regular expressions

An elegant and intuitive toolkit that makes regex operations simple and smooth. Provides comprehensive functions for validation, extraction, and cleaning of data using regular expressions in Python.

🚀 Quick Start

pip install re-kiss          # Requires Python 3.7+

from re_kiss import validate_email, extract_emails, strip_html

# Validation
print(validate_email("[e-mail]"))  # True

# Extraction
text = "Contact: [e-mail] and [e-mail]"
print(extract_emails(text))  # ['[e-mail]', '[e-mail]']

# Cleaning
html = "<p>Hello <b>World</b></p>"
print(strip_html(html))  # "Hello World"

📋 Modules

validators — Validate data

from re_kiss import validate_email, validate_url, validate_phone, validate_password, validate_ip, validate_date

Function	Description
validate_email(email, strict=True)	Email address validation
validate_url(url, require_https=False)	URL validation
validate_phone(phone, locale='any')	Phone number validation ('us', 'uk', 'any')
validate_password(password, min_length=8, ...)	Password strength with configurable rules
validate_ip(ip, version=4)	IPv4/IPv6 validation
validate_date(date_str, format='iso')	Date validation ('iso', 'us', 'eu')

extractors — Extract data from text

from re_kiss import extract_emails, extract_urls, extract_phones, extract_numbers, extract_words, extract_between

Function	Description
extract_emails(text, unique=True)	Extract email addresses
extract_urls(text, unique=True)	Extract URLs
extract_phones(text, locale='any')	Extract phone numbers
extract_numbers(text, decimals=True, negative=True)	Extract numbers (int/float)
extract_words(text, min_length=1, language='all')	Extract words ('en', 'ru', 'all')
extract_between(text, start, end, include_bounds=False)	Extract text between markers

cleaners — Clean and normalize text

from re_kiss import strip_html, normalize_whitespace, remove_special_chars, remove_numbers, remove_punctuation

Function	Description
strip_html(text, keep_content=True)	Remove HTML tags
normalize_whitespace(text)	Collapse multiple spaces/newlines
remove_special_chars(text, keep_spaces=True, ...)	Remove special characters
remove_numbers(text, keep_positions=False, replacement='')	Remove numbers
remove_punctuation(text, keep_sentences=True)	Remove punctuation

📖 Examples

from re_kiss import *

# Email validation
validate_email("[e-mail]")           # True
validate_email("invalid-email")              # False
validate_email("test@mail", strict=False)    # True (loose mode)

# Password strength
validate_password("Weak")                    # False
validate_password("Str0ng!Pass", 
                  min_length=8,
                  require_special=True)      # True

# Extract numbers
extract_numbers("Price: $19.99, Qty: 5")    # [19.99, 5]
extract_numbers("No decimals here", 
                decimals=False)              # []

# Extract between markers
extract_between("hello {world} foo", 
                "{", "}")                    # ['world']
extract_between("<!-- comment -->", 
                "<!--", "-->")              # [' comment ']

# HTML cleaning
strip_html("<p>Hello <b>World</b></p>")     # "Hello World"
strip_html("<div>Text</div>", 
           keep_content=False)               # ""

# Text normalization
normalize_whitespace("Hello    world!\t\n")  # "Hello world!"
remove_special_chars("User123! $pecial")     # "User pecial"
remove_punctuation("Hello, world!")          # "Hello world"

Advanced — RegexPattern class

from re_kiss import RegexPattern

# Create reusable pattern
pattern = RegexPattern(r'\b[A-Z][a-z]+\b')
pattern.findall("Alice Bob and Charlie")     # ['Alice', 'Bob', 'Charlie']

# Quick test
pattern.test("No names here")                # False

# Count matches
pattern.count("Alice Bob Charlie")           # 3

# Replace with function
pattern.replace("Alice Bob", str.upper)      # "ALICE BOB"

# Iterator with positions
for match in pattern.finditer("Alice Bob"):
    print(match.text, match.start, match.end)
# Alice 0 5
# Bob 6 9

🔧 Dependencies

Library	Purpose
Python Standard Library	re, typing, dataclasses

Zero external dependencies — uses only built-in Python modules.

❓ FAQ

Why re-kiss over raw re? Simplifies common regex tasks into clean, readable functions with sensible defaults. No more writing boilerplate patterns for emails, URLs, phone numbers.

Performance impact? Minimal. Patterns are compiled once and cached. The RegexPattern class wraps re.compile with a cleaner interface.

Unicode support? Full Unicode support via re.UNICODE flag. Works with Cyrillic, Latin, and other alphabets.

Custom patterns? Use RegexPattern class or pass custom strings to any function that accepts patterns.

📄 License

MIT License — see LICENSE file.

🙏 Acknowledgments

• Python's re module — The foundation
• Inspired by common regex patterns used in web scraping and data validation

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Author: Fkernel653 Repository: github.com/Fkernel653/re-kiss PyPI: pypi.org/project/re-kiss