textprettify 0.1.0

A lightweight Python library for text cleaning and formatting

TextPrettify

A lightweight Python library for text cleaning and formatting. TextPrettify provides simple, intuitive functions to manipulate and format text strings for common use cases.

Features

• Remove Extra Whitespace: Clean up text by removing leading/trailing spaces and normalizing multiple spaces
• Slugify: Convert text to URL-friendly slugs
• Reading Time Estimation: Calculate estimated reading time for text
• Capitalize Words: Apply title case with customizable exceptions
• Truncate Text: Shorten text to a maximum length with word-boundary awareness
• Remove Punctuation: Strip punctuation with optional character preservation
• Word Count: Count total or unique words in text

Installation

# Clone the repository
git clone https://github.com/yourusername/TextPrettify.git
cd TextPrettify

# Install in development mode
pip install -e .

Quick Start

from textprettify import (
    remove_extra_whitespace,
    slugify,
    get_reading_time,
    capitalize_words,
    truncate_text,
    remove_punctuation,
    count_words
)

# Clean up messy whitespace
text = "  Hello    World  "
clean_text = remove_extra_whitespace(text)
print(clean_text)  # "Hello World"

# Create URL-friendly slugs
title = "My Awesome Post!"
slug = slugify(title)
print(slug)  # "my-awesome-post"

# Estimate reading time
article = "Lorem ipsum " * 200
reading_time = get_reading_time(article)
print(reading_time)  # "2 mins read"

# Capitalize with exceptions
title = "a tale of two cities"
formatted = capitalize_words(title, exceptions=['a', 'of'])
print(formatted)  # "A Tale of Two Cities"

# Truncate long text
text = "The quick brown fox jumps over the lazy dog"
short = truncate_text(text, max_length=20)
print(short)  # "The quick brown..."

# Remove punctuation
text = "Hello, World!"
clean = remove_punctuation(text)
print(clean)  # "Hello World"

# Count words
text = "Hello world hello"
total = count_words(text)
unique = count_words(text, unique=True)
print(f"Total: {total}, Unique: {unique}")  # "Total: 3, Unique: 2"

API Reference

remove_extra_whitespace(text: str) -> str

Remove extra whitespace from text, including leading/trailing spaces and multiple consecutive spaces.

Parameters:

• text (str): The input text to clean

Returns:

• str: Text with normalized whitespace

Example:

remove_extra_whitespace("  Hello    World  ")
# "Hello World"

slugify(text: str, separator: str = '-', lowercase: bool = True) -> str

Convert text to a URL-friendly slug.

Parameters:

• text (str): The input text to slugify
• separator (str): Character to use as separator (default: '-')
• lowercase (bool): Convert to lowercase (default: True)

Returns:

• str: URL-friendly slug

Example:

slugify("My Awesome Post!")
# "my-awesome-post"

slugify("Hello, World!", separator='_')
# "hello_world"

get_reading_time(text: str, words_per_minute: int = 200, include_unit: bool = True) -> str | int

Calculate estimated reading time for text.

Parameters:

• text (str): The input text to analyze
• words_per_minute (int): Average reading speed (default: 200)
• include_unit (bool): Return formatted string with unit (default: True)

Returns:

• str | int: Reading time as formatted string or integer (minutes)

Example:

get_reading_time("Hello world " * 100)
# "1 min read"

get_reading_time("Hello world " * 100, include_unit=False)
# 1

capitalize_words(text: str, exceptions: Optional[list[str]] = None) -> str

Capitalize the first letter of each word (title case).

Parameters:

• text (str): The input text to capitalize
• exceptions (list[str], optional): List of words to keep lowercase

Returns:

• str: Text with capitalized words

Example:

capitalize_words("the quick brown fox")
# "The Quick Brown Fox"

capitalize_words("a tale of two cities", exceptions=['a', 'of'])
# "A Tale of Two Cities"

truncate_text(text: str, max_length: int, suffix: str = '...', whole_words: bool = True) -> str

Truncate text to a maximum length.

Parameters:

• text (str): The input text to truncate
• max_length (int): Maximum length of output text
• suffix (str): String to append to truncated text (default: '...')
• whole_words (bool): Only break at word boundaries (default: True)

Returns:

• str: Truncated text

Example:

truncate_text("The quick brown fox jumps", 15)
# "The quick..."

truncate_text("The quick brown fox jumps", 15, whole_words=False)
# "The quick br..."

remove_punctuation(text: str, keep: Optional[str] = None) -> str

Remove punctuation from text.

Parameters:

• text (str): The input text to clean
• keep (str, optional): String of punctuation characters to keep

Returns:

• str: Text without punctuation

Example:

remove_punctuation("Hello, World!")
# "Hello World"

remove_punctuation("[e-mail]", keep='@.')
# "[e-mail]"

count_words(text: str, unique: bool = False) -> int

Count words in text.

Parameters:

• text (str): The input text to analyze
• unique (bool): Count only unique words (default: False)

Returns:

• int: Word count

Example:

count_words("Hello world hello")
# 3

count_words("Hello world hello", unique=True)
# 2

Running Tests

# Run all tests
python -m pytest tests/

# Run with coverage
python -m pytest tests/ --cov=textprettify

# Run specific test file
python -m pytest tests/test_core.py

Or using unittest:

python -m unittest discover tests

Examples

Check out the examples/ directory for more detailed usage examples:

• examples/basic_usage.py - Basic usage examples for all functions
• examples/blog_post_formatter.py - Format blog post metadata
• examples/url_generator.py - Generate clean URLs from titles

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Author

Sajith

Version

0.1.0