hygiea 1.0.0

A comprehensive Python toolkit for data cleaning and preprocessing

🚀 Hygiea: Your Data's New Superpower 🧹

Tired of wrestling with messy spreadsheets and endless cleaning scripts? Hygiea is a comprehensive Python toolkit that handles EVERYTHING for data cleaning, preprocessing, and analysis.

✨ Why Hygiea?

Stop wasting hours on repetitive data cleaning tasks. Hygiea transforms raw, messy data into clean, model-ready insights in minutes, not hours.

🎯 Key Features

• 🆔 Standardize: Auto-lowercase and clean column names
• 🔄 Convert: Detect/convert dates, numeric strings, booleans
• 💧 Impute: Median/mode, KNN, or MICE imputation
• ⚖️ Winsorize: Cap outliers via IQR or z-score
• 🧩 Encode: One-hot, target, or label encoding
• 📊 EDA: Summary stats, missing-value report, correlation
• 🌐 Profiling: Interactive HTML reports with one line
• 🔄 Pipeline-Ready: Drop-in sklearn transformer

🚀 Quick Start

Installation

pip install -e .

Basic Usage

import pandas as pd
import hygiea as hg

# Load your messy data
df = pd.DataFrame({
    'User ID': [1, 2, 3],
    'First Name': ['John', 'Jane', 'Bob'],
    'Annual Income ($)': [50000, None, 75000]
})

# Clean it in one line!
df_clean = hg.clean_data(df)
print(df_clean.columns.tolist())
# Output: ['user_id', 'first_name', 'annual_income']

# Generate beautiful HTML report
hg.profile_data(df, output_file='report.html')

# Get smart cleaning suggestions
suggestions = hg.suggest_cleaning_strategy(df)
print(f"Recommended: {suggestions['recommended_profile']}")

Advanced Pipeline Integration

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
import hygiea as hg

# Seamless sklearn integration
pipeline = Pipeline([
    ('clean', hg.get_transformer()),
    ('model', RandomForestClassifier())
])

# Train with messy data - Hygiea handles the rest!
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

📊 Cleaning Profiles

Choose the right cleaning intensity for your data:

# Gentle cleaning (minimal changes)
df_gentle = hg.clean_data(df, profile='gentle')

# Default cleaning (balanced approach)  
df_default = hg.clean_data(df, profile='default')

# Aggressive cleaning (thorough transformation)
df_aggressive = hg.clean_data(df, profile='aggressive')

# Custom cleaning (full control)
custom_config = {
    'standardize_columns': True,
    'convert_types': True,
    'impute': True,
    'handle_outliers': True
}
df_custom = hg.clean_data(df, profile='custom', custom_config=custom_config)

🎛️ Modular Usage

Use individual components for specific tasks:

from hygiea import HygieaStandardizer

# Standardize column names
standardizer = HygieaStandardizer()
df = standardizer.standardize_columns(df)

# Get suggestions without applying
suggestions = standardizer.suggest_column_names(df)
print(suggestions)  # {'Old Name': 'new_name', ...}

🧪 Testing

Run the comprehensive test suite:

python test_hygiea.py

📄 License

MIT License - feel free to use in your projects!

🌟 Show Your Support

If Hygiea saves you time and frustration, please ⭐ star this repo!

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🚀 Stop wasting hours on data cleaning!
Turn raw data into model-ready insights in minutes with Hygiea! 🧹✨