91life-ds-lib 1.0.0

Professional Data Science Library for ML Engineers and Researchers

91life Data Science Library

Overview

The 91life Data Science Library is a professional, production-ready Python library designed for ML engineers and researchers at 91.life. It provides comprehensive tools for data loading, exploration, feature selection, preprocessing, visualization, and automated reporting.

Key Features

• Async Data Loading: Support for multiple formats (CSV, Parquet, JSON, Excel) with cloud storage integration (AWS S3, Google Cloud, MinIO)
• Comprehensive Data Exploration: Automated data quality assessment, missing data analysis, and statistical profiling
• Advanced Feature Selection: Multiple methods including variance, correlation, mutual information, tree-based, L1 regularization, and consensus selection
• Data Preprocessing: Complete pipeline for missing value handling, outlier treatment, scaling, encoding, and class imbalance
• Rich Visualizations: Interactive plots with Plotly, static plots with Matplotlib/Seaborn, and automated dashboards
• Automated Reporting: Integration with YData Profiling and Sweetviz, plus custom HTML/JSON reports
• Clean Architecture: Domain-Driven Design (DDD) patterns with comprehensive logging and error handling
• Performance Optimized: Memory-efficient chunked processing for large datasets

Installation

Basic Installation

pip install 91life-ds-lib

With Cloud Storage Support

pip install 91life-ds-lib[cloud]

With Profiling Tools

pip install 91life-ds-lib[profiling]

Development Installation

git clone https://github.com/91life/91life-ds-lib.git
cd 91life-ds-lib
pip install -e ".[dev]"

Quickstart

from ninetyone_life_ds import DataLoader, DataExplorer, FeatureSelector

# Load data efficiently
loader = DataLoader()
data = loader.load_dataset('your_data.csv')

# Explore data comprehensively
explorer = DataExplorer()
basic_info = explorer.analyze_basic_info(data)
missing_analysis = explorer.analyze_missing_data(data)
readiness_score = explorer.calculate_data_readiness_score(data)

# Select features using consensus method
selector = FeatureSelector()
selected_features = selector.consensus_feature_selection(
    data, 
    target_col='target',
    task_type='classification'
)

print(f"Data readiness: {readiness_score['overall_readiness']}/100")
print(f"Selected features: {len(selected_features['selected_features'])}")

Full Example

See examples/complete_workflow.py for a comprehensive demonstration of all library capabilities.

API Overview

Core Modules

• DataLoader: Efficient data loading with cloud storage support
• DataExplorer: Comprehensive data exploration and quality assessment
• FeatureSelector: Advanced feature selection with multiple algorithms
• DataPreprocessor: Complete preprocessing pipeline
• Visualizer: Rich visualizations and interactive plots
• ReportGenerator: Automated report generation and profiling

Main Classes

• DataLoader: Handles data loading from various sources and formats
• DataExplorer: Performs comprehensive data analysis and quality assessment
• FeatureSelector: Implements multiple feature selection algorithms
• DataPreprocessor: Provides complete data preprocessing pipeline
• Visualizer: Creates professional visualizations and plots
• ReportGenerator: Generates comprehensive analysis reports

Development Setup

Prerequisites

• Python 3.8+
• pip or conda

Setup

# Clone repository
git clone https://github.com/91life/91life-ds-lib.git
cd 91life-ds-lib

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

# Run tests
pytest

# Run linting
flake8 src/ tests/

# Format code
black src/ tests/

# Type checking
mypy src/

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=src/ninetyone_life_ds --cov-report=html

# Run specific test file
pytest tests/test_data_explorer.py -v

Contributing Guidelines

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests and linting (pytest && flake8 src/ tests/)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Code Style

• Follow PEP 8 guidelines
• Use type hints for all functions
• Write comprehensive docstrings (Google style)
• Ensure all tests pass
• Maintain test coverage above 90%

License

This project is licensed under the 91Life License - see the LICENSE file for details.

Contact

• Company: 91.life
• Author: Shpat Dobraj
• Email: [e-mail]
• Issues: GitHub Issues

Company Insights

91.life is a technology company focused on data science and machine learning solutions. The company provides professional tools and services for data analysis, with a focus on healthcare and life sciences applications.

For more information about 91.life's services and team, visit https://91.life.