TextPrettify

A comprehensive Python library for text formatting, transformation, and analysis. TextPrettify provides specialized, easy-to-use classes for manipulating and analyzing text strings for common use cases.

Features

Formatters

• BasicFormatter: Core text operations (whitespace, slugify, reading time, capitalization, truncation, punctuation, word counting)
• CaseFormatter: Case conversions (snake_case, camelCase, PascalCase, CONSTANT_CASE, kebab-case, Title Case)
• TransformationFormatter: Text transformations (reversal, line operations, find/replace, highlighting, acronyms, wrapping)
• GenerationFormatter: Text generation (Lorem Ipsum, number spelling, currency, percentages)
• NormalizationFormatter: Text normalization (Unicode, accents, smart quotes)

Analyzers

• CharacterAnalyzer: Character-level analysis (counts, types)
• SentenceAnalyzer: Sentence extraction and analysis
• ReadabilityAnalyzer: Readability metrics (Flesch Reading Ease, Flesch-Kincaid Grade)
• StatisticsAnalyzer: Word statistics and frequency analysis
• LanguageAnalyzer: Basic language detection

Installation

# From PyPI (when published)
pip install textprettify

# From source
git clone https://github.com/mmssajith/TextPrettify.git
cd TextPrettify
pip install -e .

# With development dependencies
pip install -e ".[dev]"

Quick Start

Basic Formatting

from textprettify import BasicFormatter

# Clean up messy whitespace
formatter = BasicFormatter("  Hello    World  ")
print(formatter.remove_extra_whitespace())  # "Hello World"

# Create URL-friendly slugs
formatter = BasicFormatter("My Awesome Post!")
print(formatter.slugify())  # "my-awesome-post"

# Estimate reading time
formatter = BasicFormatter("Lorem ipsum " * 200)
print(formatter.get_reading_time())  # "2 mins read"

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

# Truncate long text
formatter = BasicFormatter("The quick brown fox jumps over the lazy dog")
print(formatter.truncate(max_length=20))  # "The quick brown..."

# Count words
formatter = BasicFormatter("Hello world hello")
print(formatter.count_words())  # 3
print(formatter.count_words(unique=True))  # 2

Case Conversions

from textprettify import CaseFormatter

formatter = CaseFormatter("Hello World")
print(formatter.to_snake_case())      # "hello_world"
print(formatter.to_camel_case())      # "helloWorld"
print(formatter.to_pascal_case())     # "HelloWorld"
print(formatter.to_constant_case())   # "HELLO_WORLD"
print(formatter.to_kebab_case())      # "hello-world"
print(formatter.to_title_case(exceptions=['the', 'of']))  # "Hello World"

Text Transformations

from textprettify import TransformationFormatter

# Text reversal
formatter = TransformationFormatter("Hello World")
print(formatter.reverse_characters())  # "dlroW olleH"
print(formatter.reverse_words())       # "World Hello"

# Line operations
formatter = TransformationFormatter("apple\nbanana\napple\ncherry")
print(formatter.deduplicate_lines())   # "apple\nbanana\ncherry"
print(formatter.sort_lines())          # "apple\nbanana\ncherry"

# Find and replace
formatter = TransformationFormatter("Hello World, hello Python")
print(formatter.find_and_replace('hello', 'Hi', case_sensitive=False))
# "Hi World, Hi Python"

# Regex replace
formatter = TransformationFormatter("I have 5 apples and 10 oranges")
print(formatter.find_and_replace(r'\d+', 'X', regex=True))
# "I have X apples and X oranges"

# Extract acronyms
formatter = TransformationFormatter("NASA and FBI are USA organizations")
print(formatter.extract_acronyms())  # ['NASA', 'FBI', 'USA']

# Text wrapping
formatter = TransformationFormatter("Very long text here...")
print(formatter.wrap_text(width=40))

Text Generation

from textprettify import GenerationFormatter

# Lorem Ipsum
lorem = GenerationFormatter.lorem_ipsum(paragraphs=2)
print(lorem)

# Spell out numbers
formatter = GenerationFormatter("I have 5 apples and 10 oranges")
print(formatter.spell_out_numbers())  # "I have five apples and ten oranges"

# Format currency
formatter = GenerationFormatter("The price is 1234.5")
print(formatter.format_currency())     # "The price is $1,234.50"
print(formatter.format_currency('€'))  # "The price is €1,234.50"

# Format percentages
formatter = GenerationFormatter("Success rate is 0.95")
print(formatter.format_percentage())  # "Success rate is 95.0%"

Text Normalization

from textprettify import NormalizationFormatter

# Remove accents
formatter = NormalizationFormatter("café résumé")
print(formatter.remove_accents())  # "cafe resume"

# Unicode normalization
formatter = NormalizationFormatter("café")
print(formatter.normalize_unicode('NFC'))

# Smart quotes
formatter = NormalizationFormatter('"Hello World"')
print(formatter.to_smart_quotes())      # ""Hello World""
print(formatter.to_straight_quotes())   # '"Hello World"'

Text Analysis

from textprettify import (
    CharacterAnalyzer,
    SentenceAnalyzer,
    ReadabilityAnalyzer,
    StatisticsAnalyzer,
    LanguageAnalyzer
)

text = "Python is a high-level programming language. It's easy to learn."

# Character analysis
char_analyzer = CharacterAnalyzer(text)
counts = char_analyzer.get_all_counts()
print(f"Total characters: {counts['total']}")
print(f"Letters: {counts['letters']}")
print(f"Digits: {counts['digits']}")

# Sentence analysis
sent_analyzer = SentenceAnalyzer(text)
print(f"Sentences: {sent_analyzer.count()}")
print(f"Average length: {sent_analyzer.average_length()} words")

# Readability metrics
read_analyzer = ReadabilityAnalyzer(text)
scores = read_analyzer.get_scores()
print(f"Reading ease: {scores['reading_ease']}")
print(f"Grade level: {scores['grade_level']}")
print(f"Interpretation: {read_analyzer.interpret_reading_ease()}")

# Text statistics
stats_analyzer = StatisticsAnalyzer(text)
stats = stats_analyzer.get_statistics()
print(f"Total words: {stats['word_count']}")
print(f"Unique words: {stats['unique_word_count']}")
print(f"Lexical diversity: {stats['lexical_diversity']}")

# Word frequency
word_freq = stats_analyzer.word_frequency(top_n=5)
print(f"Top 5 words: {word_freq}")

# Language detection
lang_analyzer = LanguageAnalyzer(text)
result = lang_analyzer.detect()
print(f"Language: {lang_analyzer.get_language_name()} ({result['language']})")
print(f"Confidence: {result['confidence']}")

API Reference

BasicFormatter

BasicFormatter(text: str)

Methods:

• remove_extra_whitespace() -> str: Remove extra whitespace
• slugify(separator: str = '-', lowercase: bool = True) -> str: Convert to URL slug
• get_reading_time(words_per_minute: int = 200, include_unit: bool = True) -> str | int: Estimate reading time
• capitalize_words(exceptions: list[str] = None) -> str: Capitalize words with exceptions
• truncate(max_length: int, suffix: str = '...', whole_words: bool = True) -> str: Truncate text
• remove_punctuation(keep: str = None) -> str: Remove punctuation
• count_words(unique: bool = False) -> int: Count words

CaseFormatter

CaseFormatter(text: str)

Methods:

• to_snake_case() -> str: Convert to snake_case
• to_camel_case() -> str: Convert to camelCase
• to_pascal_case() -> str: Convert to PascalCase
• to_constant_case() -> str: Convert to CONSTANT_CASE
• to_kebab_case() -> str: Convert to kebab-case
• to_title_case(exceptions: list[str] = None) -> str: Convert to Title Case

TransformationFormatter

TransformationFormatter(text: str)

Methods:

• reverse_characters() -> str: Reverse character order
• reverse_words() -> str: Reverse word order
• add_letter_spacing(separator: str = ' ') -> str: Add spacing between letters
• remove_blank_lines() -> str: Remove blank lines
• deduplicate_lines() -> str: Remove duplicate lines
• sort_lines(reverse: bool = False) -> str: Sort lines
• find_and_replace(pattern: str, replacement: str, case_sensitive: bool = True, regex: bool = False) -> str: Find and replace text
• highlight_markdown(words: list[str], style: str) -> str: Highlight words in markdown
• highlight_html(words: list[str], tag: str) -> str: Highlight words in HTML
• extract_acronyms() -> list[str]: Extract acronyms
• wrap_text(width: int) -> str: Wrap text to width

GenerationFormatter

GenerationFormatter(text: str)

Static Methods:

• lorem_ipsum(paragraphs: int = 1, sentences_per_paragraph: int = 5) -> str: Generate Lorem Ipsum

Instance Methods:

• spell_out_numbers(max_number: int = 100) -> str: Spell out numbers
• format_currency(symbol: str = '$') -> str: Format currency
• format_percentage(decimals: int = 1) -> str: Format percentages

NormalizationFormatter

NormalizationFormatter(text: str)

Methods:

• normalize_unicode(form: str = 'NFC') -> str: Normalize Unicode (NFC, NFD, NFKC, NFKD)
• remove_accents() -> str: Remove accents from text
• to_smart_quotes() -> str: Convert to smart quotes
• to_straight_quotes() -> str: Convert to straight quotes

Analyzers

See the Quick Start section above for analyzer usage examples.

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=textprettify

# Run specific test file
pytest tests/formatters/test_basic_formatter.py

# Run specific test class
pytest tests/formatters/test_basic_formatter.py::TestSlugify

Examples

Check out the examples/ directory for comprehensive usage examples:

• basic_usage.py - Basic formatting operations
• text_transformation_example.py - Case conversions and transformations
• text_generation_example.py - Text generation and manipulation
• text_analysis_example.py - Text analysis and statistics
• blog_post_formatter.py - Format blog post metadata
• url_generator.py - Generate clean URLs from titles

Project Structure

textprettify/
├── formatters/
│   ├── basic_formatter.py
│   ├── case_formatter.py
│   ├── transformation_formatter.py
│   ├── generation_formatter.py
│   └── normalization_formatter.py
├── analyzers/
│   ├── character_analyzer.py
│   ├── sentence_analyzer.py
│   ├── readability_analyzer.py
│   ├── statistics_analyzer.py
│   └── language_analyzer.py
tests/
├── formatters/
└── analyzers/
examples/

Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Development Setup

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

# Install in development mode with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=textprettify --cov-report=html

License

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

Author

Sajith

Development

Code Quality Tools

This project uses pre-commit hooks to maintain code quality:

# Install pre-commit hooks
pre-commit install

# Run hooks manually on all files
pre-commit run --all-files

Configured hooks:

• Ruff: Fast Python linter and formatter
• Mypy: Static type checking

Running Pre-commit Checks

The pre-commit hooks will run automatically on every commit. You can also run them manually:

# Run all hooks
pre-commit run --all-files

# Run specific hook
pre-commit run ruff --all-files
pre-commit run mypy --all-files

Changelog

See CHANGELOG.md for detailed version history.

Latest Release: v0.2.0 - Added comprehensive text analysis tools, pre-commit hooks, and enhanced formatters.