funputer 1.3.7

Intelligent imputation analysis with automatic data validation and metadata inference

FunPuter - Intelligent Imputation Analysis

Intelligent imputation analysis with automatic data validation and metadata inference

FunPuter analyzes your data and recommends the best imputation methods based on data patterns, missing mechanisms, and metadata constraints. Get intelligent suggestions with confidence scores to handle missing data professionally.

🚀 Quick Start

Installation

pip install funputer

30-Second Example

Auto-Inference Mode (Zero Configuration)

import funputer

# Point to your CSV - FunPuter figures out everything automatically
suggestions = funputer.analyze_imputation_requirements("your_data.csv")

# Get intelligent suggestions with confidence scores
for suggestion in suggestions:
    if suggestion.missing_count > 0:
        print(f"📊 {suggestion.column_name}: {suggestion.proposed_method}")
        print(f"   Confidence: {suggestion.confidence_score:.3f}")
        print(f"   Reason: {suggestion.rationale}")
        print(f"   Missing: {suggestion.missing_count} ({suggestion.missing_percentage:.1f}%)")

Production Mode (Full Control)

import funputer
from funputer.models import ColumnMetadata

# Define your data structure with constraints
metadata = [
    ColumnMetadata('customer_id', 'integer', unique_flag=True, nullable=False),
    ColumnMetadata('age', 'integer', min_value=18, max_value=100),
    ColumnMetadata('income', 'float', min_value=0),
    ColumnMetadata('category', 'categorical', allowed_values='A,B,C'),
]

# Get production-grade suggestions
suggestions = funputer.analyze_dataframe(your_dataframe, metadata)

🎯 Key Features

• 🤖 Automatic Metadata Inference - Intelligent data type and constraint detection
• 📊 Missing Data Analysis - MCAR, MAR, MNAR mechanism detection
• ⚡ Data Validation - Real-time constraint checking and validation
• 🎯 Smart Recommendations - Context-aware imputation method suggestions
• 📈 Confidence Scoring - Transparent reliability estimates for each recommendation
• 🛡️ Pre-flight Checks - Comprehensive data validation before analysis
• 💻 CLI & Python API - Flexible usage via command line or programmatic access

📊 Data Validation System

Comprehensive validation runs automatically to prevent crashes and guide your workflow:

• File validation: Format detection, encoding, accessibility
• Structure validation: Column analysis, data type inference
• Memory estimation: Resource usage prediction
• Advisory recommendations: Guided workflow suggestions

Independent Usage:

# Basic validation check
funputer preflight -d your_data.csv

# With custom options  
funputer preflight -d data.csv --sample-rows 5000 --encoding utf-8

# JSON report output
funputer preflight -d data.csv --json-out report.json

Exit Codes:

• 0: Ready for analysis
• 2: OK with warnings (can proceed)
• 10: Hard error (cannot proceed)

💻 Command Line Interface

# Generate metadata template from your data
funputer init -d data.csv -o metadata.csv

# Analyze with auto-inference  
funputer analyze -d data.csv

# Analyze with custom metadata
funputer analyze -d data.csv -m metadata.csv --verbose

# Data quality check first
funputer preflight -d data.csv

📚 Usage Examples

Basic Analysis

import funputer

# Simple analysis with auto-inference
suggestions = funputer.analyze_imputation_requirements("sales_data.csv")

# Display recommendations
for suggestion in suggestions:
    print(f"Column: {suggestion.column_name}")
    print(f"Method: {suggestion.proposed_method}")  
    print(f"Confidence: {suggestion.confidence_score:.3f}")
    print(f"Missing: {suggestion.missing_count} values")
    print()

Advanced Configuration

from funputer.models import ColumnMetadata, AnalysisConfig
from funputer.simple_analyzer import SimpleImputationAnalyzer

# Custom metadata with business rules
metadata = [
    ColumnMetadata('product_id', 'string', unique_flag=True, max_length=10),
    ColumnMetadata('price', 'float', min_value=0, max_value=10000),
    ColumnMetadata('category', 'categorical', allowed_values='Electronics,Books,Clothing'),
    ColumnMetadata('rating', 'float', min_value=1.0, max_value=5.0),
]

# Custom analysis configuration
config = AnalysisConfig(
    missing_percentage_threshold=0.3,  # 30% threshold
    skip_columns=['internal_id'],
    outlier_threshold=0.1
)

# Run analysis
analyzer = SimpleImputationAnalyzer(config)
suggestions = analyzer.analyze_dataframe(df, metadata)

Industry-Specific Examples

E-commerce Analytics

metadata = [
    ColumnMetadata('customer_id', 'integer', unique_flag=True, nullable=False),
    ColumnMetadata('age', 'integer', min_value=13, max_value=120),
    ColumnMetadata('purchase_amount', 'float', min_value=0),
    ColumnMetadata('customer_segment', 'categorical', allowed_values='Premium,Standard,Basic'),
]
suggestions = funputer.analyze_dataframe(customer_df, metadata)

Healthcare Data

metadata = [
    ColumnMetadata('patient_id', 'integer', unique_flag=True, nullable=False),
    ColumnMetadata('age', 'integer', min_value=0, max_value=150),
    ColumnMetadata('blood_pressure', 'integer', min_value=50, max_value=300),
    ColumnMetadata('diagnosis', 'categorical', nullable=False),
]
config = AnalysisConfig(missing_threshold=0.05)  # Low tolerance for healthcare
suggestions = funputer.analyze_dataframe(patient_df, metadata, config)

Financial Risk Assessment

metadata = [
    ColumnMetadata('application_id', 'integer', unique_flag=True, nullable=False),
    ColumnMetadata('credit_score', 'integer', min_value=300, max_value=850),
    ColumnMetadata('debt_to_income', 'float', min_value=0.0, max_value=10.0),
    ColumnMetadata('loan_purpose', 'categorical', allowed_values='home,auto,personal,business'),
]
# Skip sensitive columns
config = AnalysisConfig(skip_columns=['ssn', 'account_number'])
suggestions = funputer.analyze_dataframe(loan_df, metadata, config)

⚙️ Requirements

• Python: 3.9 or higher
• Dependencies: pandas, numpy, scipy, pydantic, click, pyyaml

🔧 Installation from Source

git clone https://github.com/RajeshRamachander/funputer.git
cd funputer
pip install -e .

📚 Documentation

• API Reference: Complete docstrings and type hints throughout the codebase
• Examples: See usage examples above and in the codebase
• Test Coverage: 77% coverage with comprehensive test suite

🤝 Contributing

We welcome contributions! Please feel free to submit issues, feature requests, or pull requests.

📄 License

MIT License - see LICENSE file for details.

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Focus: Get intelligent imputation recommendations, not complex infrastructure.