tidyname 0.1.0

Intelligent company name cleaning for Python

TidyName

Intelligent company name cleaning for Python.

TidyName is a Python package that intelligently removes legal entity terms and organization type indicators from company names while preserving cases where these terms are part of the actual business name.

Features

• Smart Detection: Identifies and removes corporate suffixes (LLC, Inc., Ltd., etc.)
• Intelligent Preservation: Preserves terms when they're part of brand names (e.g., "The Limited")
• Confidence Scoring: Provides confidence levels for each cleaning decision
• International Support: Handles international corporate suffixes (GmbH, S.A., etc.)
• Batch Processing: Clean multiple company names efficiently
• Configurable: Customize behavior through configuration options
• Pure Python: No external dependencies required
• Type Safety: Full type annotations for better IDE support

Requirements

• Python 3.13+

Installation

pip install tidyname

# For development
git clone https://github.com/your-repo/tidyname.git
cd tidyname
uv install

Quick Start

from tidyname import Cleaner

# Initialize the cleaner
cleaner = Cleaner()

# Clean a single company name
result = cleaner.clean("Apple Inc.")

print(result.original)         # "Apple Inc."
print(result.cleaned)          # "Apple"
print(result.confidence)       # 0.95
print(result.confidence_level) # "high"
print(result.changes_made)     # True
print(result.reason)           # "Removed: Inc."

Advanced Usage

Batch Processing

from tidyname import Cleaner

cleaner = Cleaner()

companies = [
    "Apple Inc.",
    "Microsoft Corporation", 
    "Google LLC",
    "The Limited",  # Will be preserved
    "Amazon"        # No changes needed
]

results = cleaner.clean_batch(companies)

for result in results:
    print(f"{result.original} → {result.cleaned}")

Configuration Options

from tidyname import Cleaner, CleanerConfig

# Custom configuration
config = CleanerConfig(
    remove_corporate_suffixes=True,     # Enable/disable suffix removal
    preserve_known_brands=True,         # Preserve known brand names
    min_confidence_threshold=0.7        # Minimum confidence for changes
)

cleaner = Cleaner(config=config)

# Or configure after initialization
cleaner.configure(
    preserve_known_brands=False,
    min_confidence_threshold=0.8
)

Supported Terms

Corporate Suffixes

• Corporation: Company, Incorporated, Corporation, Corp., Corp, Inc., Inc
• Limited Liability: LLC, L.L.C., PLC, P.L.C.
• Limited: Limited, Ltd., Ltd, Co., Co
• Partnership: & Co., & Co, LLP, L.L.P.
• Professional: Professional Corporation, P.C., PC

International Suffixes

• German: GmbH, AG
• French: S.A., S.A
• Dutch: N.V., B.V.
• Italian: S.r.l., S.p.A.

Examples

Basic Cleaning

from tidyname import Cleaner

cleaner = Cleaner()

# Standard corporate suffixes
print(cleaner.clean("Apple Inc.").cleaned)           # "Apple"
print(cleaner.clean("Microsoft Corporation").cleaned) # "Microsoft"
print(cleaner.clean("Google LLC").cleaned)           # "Google"

# International suffixes
print(cleaner.clean("Siemens AG").cleaned)           # "Siemens"
print(cleaner.clean("L'Oréal S.A.").cleaned)        # "L'Oréal"

# Multiple suffixes
print(cleaner.clean("Tech Solutions Inc. LLC").cleaned) # "Tech Solutions"

Brand Preservation

from tidyname import Cleaner

cleaner = Cleaner()

# These will be preserved as they're known brands
result = cleaner.clean("The Limited")
print(result.cleaned)      # "The Limited"
print(result.changes_made) # False

result = cleaner.clean("Limited Brands")
print(result.cleaned)      # "Limited Brands"
print(result.changes_made) # False

Confidence and Reasoning

from tidyname import Cleaner

cleaner = Cleaner()

result = cleaner.clean("Apple Inc.")

print(f"Confidence: {result.confidence}")           # 0.95
print(f"Level: {result.confidence_level}")          # "high"
print(f"Reasoning: {result.reason}")                # "Removed: Inc."

# Low confidence example
result = cleaner.clean("Limited Edition")
print(f"Confidence: {result.confidence}")           # Lower score
print(f"Reasoning: {result.reason}")                # Preservation reasoning

API Reference

Cleaner Class

__init__(config: CleanerConfig | None = None)

Initialize the cleaner with optional configuration.

clean(company_name: str) -> CleaningResult

Clean a single company name.

Parameters:

• company_name: The company name to clean

Returns:

• CleaningResult object with cleaning results and metadata

clean_batch(company_names: list[str]) -> list[CleaningResult]

Clean multiple company names.

Parameters:

• company_names: List of company names to clean

Returns:

• List of CleaningResult objects

configure(**kwargs) -> None

Update configuration settings.

CleaningResult

Result object containing:

• original: Original company name
• cleaned: Cleaned company name
• confidence: Confidence score (0.0 to 1.0)
• confidence_level: "high", "medium", or "low"
• changes_made: Boolean indicating if changes were made
• reason: Human-readable explanation of the decision

CleanerConfig

Configuration object with:

• remove_corporate_suffixes: Enable suffix removal (default: True)
• preserve_known_brands: Preserve known brand names (default: True)
• min_confidence_threshold: Minimum confidence for changes (default: 0.5)

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite: uv run pytest
6. Submit a pull request

License

MIT License - see LICENSE file for details.