tukuy 0.0.2

A flexible data transformation library with a plugin system

🌀 Tukuy

A flexible data transformation library with a plugin system for Python.

🚀 Overview

Tukuy (meaning "to transform" or "to become" in Quechua) is a powerful and extensible data transformation library that makes it easy to manipulate, validate, and extract data from various formats. With its plugin architecture, Tukuy provides a unified interface for working with text, HTML, JSON, dates, numbers, and more.

✨ Features

• 🧩 Plugin System: Easily extend functionality with custom plugins
• 🔄 Chainable Transformers: Compose multiple transformations in sequence
• 🧪 Type-safe Transformations: With built-in validation
• 📊 Rich Set of Built-in Transformers:
  • 📝 Text manipulation (case conversion, trimming, regex, etc.)
  • 🌐 HTML processing and extraction
  • 📅 Date parsing and calculations
  • 🔢 Numerical operations
  • ✅ Data validation
  • 📋 JSON parsing and extraction
• 🔍 Pattern-based Data Extraction: Extract structured data from HTML and JSON
• 🛡️ Error Handling: Comprehensive error handling with detailed messages

📦 Installation

pip install tukuy

🛠️ Basic Usage

from tukuy import ToolsTransformer

# Create transformer
TUKUY = ToolsTransformer()

# Basic text transformation
text = " Hello World! "
result = TUKUY.transform(text, [
    "strip",
    "lowercase",
    {"function": "truncate", "length": 5}
])
print(result)  # "hello..."

# HTML transformation
html = "<div>Hello <b>World</b>!</div>"
result = TUKUY.transform(html, [
    "strip_html_tags",
    "lowercase"
])
print(result)  # "hello world!"

# Date transformation
date_str = "2023-01-01"
age = TUKUY.transform(date_str, [
    {"function": "age_calc"}
])
print(age)  # 1

# Validation
email = "[e-mail]"
valid = TUKUY.transform(email, ["email_validator"])
print(valid)  # "[e-mail]" or None if invalid

🧩 Plugin System

Tukuy uses a plugin system to organize transformers into logical groups and make it easy to extend functionality.

📚 Built-in Plugins

• 📝 text: Basic text transformations (strip, lowercase, regex, etc.)
• 🌐 html: HTML manipulation and extraction
• 📅 date: Date parsing and calculations
• ✅ validation: Data validation and formatting
• 🔢 numerical: Number manipulation and calculations
• 📋 json: JSON parsing and extraction

🔌 Creating Custom Plugins

You can create custom plugins by extending the TransformerPlugin class:

from tukuy.plugins import TransformerPlugin
from tukuy.base import ChainableTransformer

class ReverseTransformer(ChainableTransformer[str, str]):
    def validate(self, value: str) -> bool:
        return isinstance(value, str)
    
    def _transform(self, value: str, context=None) -> str:
        return value[::-1]

class MyPlugin(TransformerPlugin):
    def __init__(self):
        super().__init__("my_plugin")
    
    @property
    def transformers(self):
        return {
            'reverse': lambda _: ReverseTransformer('reverse')
        }

# Usage
TUKUY = ToolsTransformer()
TUKUY.register_plugin(MyPlugin())

result = TUKUY.transform("hello", ["reverse"])  # "olleh"

See the example plugin for a more detailed example.

🔄 Plugin Lifecycle

Plugins can implement initialize() and cleanup() methods for setup and teardown:

class MyPlugin(TransformerPlugin):
    def initialize(self) -> None:
        super().initialize()
        # Load resources, connect to databases, etc.
    
    def cleanup(self) -> None:
        super().cleanup()
        # Close connections, free resources, etc.

🔍 Pattern-based Extraction

Tukuy provides powerful pattern-based extraction capabilities for both HTML and JSON data.

🌐 HTML Extraction

pattern = {
    "properties": [
        {
            "name": "title",
            "selector": "h1",
            "transform": ["strip", "lowercase"]
        },
        {
            "name": "links",
            "selector": "a",
            "attribute": "href",
            "type": "array"
        }
    ]
}

data = TUKUY.extract_html_with_pattern(html, pattern)

📋 JSON Extraction

pattern = {
    "properties": [
        {
            "name": "user",
            "selector": "data.user",
            "properties": [
                {
                    "name": "name",
                    "selector": "fullName",
                    "transform": ["strip"]
                }
            ]
        }
    ]
}

data = TUKUY.extract_json_with_pattern(json_str, pattern)

🚀 Use Cases

Tukuy is designed to handle a wide range of data transformation scenarios:

• 🌐 Web Scraping: Extract structured data from HTML pages
• 📊 Data Cleaning: Normalize and validate data from various sources
• 🔄 Format Conversion: Transform data between different formats
• 📝 Text Processing: Apply complex text transformations
• 🔍 Data Extraction: Extract specific information from complex structures
• ✅ Validation: Ensure data meets specific criteria

⚡ Performance Tips

• 🔗 Chain Transformations: Use chained transformations to avoid intermediate objects
• 🧩 Use Built-in Transformers: Built-in transformers are optimized for performance
• 🔍 Be Specific with Selectors: More specific selectors are faster to process
• 🛠️ Custom Transformers: For performance-critical operations, create custom transformers
• 📦 Batch Processing: Process data in batches for better performance

🛡️ Error Handling

Tukuy provides comprehensive error handling with detailed error messages:

from tukuy.exceptions import ValidationError, TransformationError, ParseError

try:
    result = TUKUY.transform(data, transformations)
except ValidationError as e:
    print(f"Validation failed: {e}")
except ParseError as e:
    print(f"Parsing failed: {e}")
except TransformationError as e:
    print(f"Transformation failed: {e}")

🤝 Contributing

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

1. 🍴 Fork the repository
2. 🌿 Create a feature branch (git checkout -b feature/amazing-feature)
3. 💻 Make your changes
4. ✅ Run tests with pytest
5. 📝 Update documentation if needed
6. 🔄 Commit your changes (git commit -m 'Add amazing feature')
7. 🚀 Push to the branch (git push origin feature/amazing-feature)
8. 🔍 Open a Pull Request