FunStrings

FunStrings is a comprehensive Python package that provides a wide range of functions for string manipulation, analysis, and transformation. It's designed to make working with strings easier and more efficient for developers, students, and educators.

Table of Contents

• Features
  • Basic String Operations
  • Text Analysis Functions
  • String Transformation Functions
  • Pattern-based Functions
  • Data Cleaning Functions
  • Text Analysis Helpers
  • ML/NLP Preprocessing
  • Validation Functions
• Installation
• Quick Start
• Documentation
• For Students and Educators
• Contributing
• License
• Connect

Features

FunStrings includes 44 utility functions organized into eight categories:

Basic String Operations

• Reverse String: Return the reversed string
• Count Vowels: Count the number of vowels in the string
• Count Consonants: Count the number of consonants
• Check Palindrome: Determine whether the string is a palindrome
• To Upper/Lower: Convert the string to uppercase or lowercase
• Word Count: Count the words in the string
• Sort Characters: Return the string with its characters sorted
• Remove Whitespace: Remove all whitespace from the string

Text Analysis Functions

• Word Frequencies: Return frequency count of each word
• Longest Word: Find the longest word in the text
• Shortest Word: Find the shortest word in the text
• Average Word Length: Calculate average word length
• Is Pangram: Check if text contains all alphabet letters

String Transformation Functions

• Snake to Camel: Convert snake_case to camelCase
• Camel to Snake: Convert camelCase to snake_case
• Rotate String: Rotate string by n positions
• Shuffle String: Randomly shuffle characters
• Reverse Words: Reverse order of words but not letters

Pattern-based Functions

• Extract Numbers: Extract all numbers from text
• Extract Emails: Extract email addresses from text
• Extract URLs: Extract URLs from text
• Mask Sensitive: Mask all but last n chars with '*'
• Find Repeated Words: Find all repeated words in text

Data Cleaning Functions

• Remove HTML Tags: Strip all HTML tags from text
• Remove Emojis: Remove emojis from text
• Remove Special Characters: Keep only letters and numbers
• Expand Contractions: Convert "don't" → "do not"
• Correct Whitespace: Remove weird spaces, tabs, newlines

Text Analysis Helpers

• Unique Words: Return list of unique words
• Most Common Word: Return most frequent word
• Sentence Count: Number of sentences in text
• Average Sentence Length: Average words per sentence
• Character Ratio: Uppercase/lowercase/number ratio

ML/NLP Preprocessing

• Generate N-grams: Generate list of n-grams
• Strip Accents: Remove accents (café → cafe)
• Lemmatize Text: Reduce words to base form
• Is ASCII: Check if text only contains ASCII

Validation Functions

• Is Valid Email: Validate if a string is a proper email
• Is Valid URL: Validate if a string is a proper URL
• Is Valid IP: Check if string is a valid IP address
• Is Valid Date: Check if a string matches a date format
• Contains Special Characters: Check if special symbols are present

Installation

You can install FunStrings directly from PyPI:

pip install funstrings

Or install from source:

pip install git+https://github.com/nilkanth02/funstrings.git

Quick Start

import funstrings

# Basic operations
text = "Hello, World!"
print(funstrings.reverse_string(text))  # !dlroW ,olleH
print(funstrings.count_vowels(text))    # 3

# Text analysis
sentence = "The quick brown fox jumps over the lazy dog"
print(funstrings.is_pangram(sentence))  # True
print(funstrings.longest_word(sentence))  # quick

# Transformations
snake = "hello_world_example"
print(funstrings.snake_to_camel(snake))  # helloWorldExample

# Pattern-based
text_with_emails = "Contact us at info@example.com or support@example.org"
print(funstrings.extract_emails(text_with_emails))  # ['info@example.com', 'support@example.org']

# Data cleaning
html_text = "<p>Hello <b>World</b></p>"
print(funstrings.remove_html_tags(html_text))  # Hello World
print(funstrings.expand_contractions("I don't know"))  # I do not know

# Text analysis helpers
print(funstrings.sentence_count("Hello! How are you? I'm fine."))  # 3
print(funstrings.most_common_word("hello world hello python"))  # hello

# ML/NLP preprocessing
print(funstrings.generate_ngrams("hello", 2))  # ['he', 'el', 'll', 'lo']
print(funstrings.strip_accents("café"))  # cafe

# Validation
print(funstrings.is_valid_email("user@example.com"))  # True
print(funstrings.is_valid_url("https://example.com"))  # True

Documentation

For detailed documentation and examples, visit the GitHub repository.

For Students and Educators

FunStrings is designed to be educational and beginner-friendly. It includes:

• Detailed docstrings with examples
• Comprehensive tutorials in the tutorials/ directory
• Example scripts in the examples/ directory
• Type hints for better IDE integration

Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Commit your changes: git commit -m 'Add some feature'
4. Push to the branch: git push origin feature-name
5. Submit a pull request

Please make sure to update tests as appropriate.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Connect

• GitHub: nilkanth02
• LinkedIn: Nilkanth Ahire