funputer 1.1.0

Simple, intelligent imputation analysis for data science

FunPuter - Intelligent Imputation Analysis

Simple, fast, intelligent recommendations for handling missing data with enhanced metadata support.

FunPuter analyzes your data and suggests the best imputation methods based on:

• Missing data mechanisms (MCAR, MAR, MNAR detection)
• Data types and statistical properties
• Business rules and column dependencies
• Enhanced metadata constraints (nullable, allowed_values, max_length)
• Adaptive thresholds based on your dataset characteristics

🚀 Quick Start

Installation

pip install funputer

Basic Usage

🤖 Auto-Inference Mode (New!)

import funimpute

# Let FunPuter intelligently infer metadata from your data
suggestions = funimpute.analyze_imputation_requirements(
    data_path="data.csv"  # No metadata file needed!
)

# Use the suggestions
for suggestion in suggestions:
    print(f"{suggestion.column_name}: {suggestion.proposed_method}")
    print(f"  Rationale: {suggestion.rationale}")
    print(f"  Confidence: {suggestion.confidence_score:.3f}")

📋 Explicit Metadata Mode (Production)

import funimpute

# For maximum accuracy, provide explicit metadata
suggestions = funimpute.analyze_imputation_requirements(
    metadata_path="metadata.csv",
    data_path="data.csv"
)

🎯 Enhanced Metadata Support (v1.1.0)

FunPuter now supports comprehensive metadata fields that actively influence imputation recommendations:

Metadata Schema

Field	Type	Description	Example
column_name	string	Column identifier	"age"
data_type	string	Data type (integer, float, string, categorical, datetime)	"integer"
nullable	boolean	Allow null values	false
min_value	number	Minimum allowed value	0
max_value	number	Maximum allowed value	120
max_length	integer	Maximum string length	50
allowed_values	string	Comma-separated list of allowed values	"A,B,C"
unique_flag	boolean	Require unique values	true
dependent_column	string	Column dependencies	"age"
business_rule	string	Custom validation rules	"Must be positive"
description	string	Human-readable description	"User age in years"

🛠️ Creating Metadata

Method 1: CLI Template Generation

# Generate a metadata template from your data
funputer init --data data.csv --output metadata.csv

# Edit the generated file to add constraints
# Then analyze with enhanced metadata
funputer analyze --data data.csv --metadata metadata.csv

Method 2: Manual CSV Creation

# metadata.csv
# column_name,data_type,nullable,min_value,max_value,max_length,allowed_values,unique_flag,dependent_column,business_rule,description
user_id,integer,false,,,50,,true,,,"Unique user identifier"
age,integer,false,0,120,,,,,Must be positive,"User age in years"
income,float,true,0,,,,,age,Higher with age,"Annual income in USD"
category,categorical,false,,,10,"A,B,C",,,,"User category classification"
email,string,true,,,255,,true,,,"User email address"

🎯 Metadata in Action

Example 1: Nullable Constraints

# When nullable=False but data has missing values
metadata = ColumnMetadata(
    column_name="age",
    data_type="integer",
    nullable=False,
    min_value=0,
    max_value=120
)

# FunPuter will:
# - Detect nullable constraint violations
# - Recommend immediate data quality fixes
# - Lower confidence score due to constraint violations

Example 2: Allowed Values

# For categorical data with specific allowed values
metadata = ColumnMetadata(
    column_name="status",
    data_type="categorical",
    allowed_values="active,inactive,pending"
)

# FunPuter will:
# - Validate all values against allowed list
# - Recommend mode imputation using only allowed values
# - Increase confidence when data respects constraints

Example 3: String Length Constraints

# For string data with length limits
metadata = ColumnMetadata(
    column_name="username",
    data_type="string",
    max_length=20,
    unique_flag=True
)

# FunPuter will:
# - Check string lengths against max_length
# - Recommend imputation respecting length limits
# - Consider uniqueness requirements in recommendations

📊 Enhanced Analysis Results

# Results now include metadata-aware 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"Rationale: {suggestion.rationale}")
    
    # New: Metadata constraint information
    if suggestion.metadata_violations:
        print(f"Violations: {suggestion.metadata_violations}")
    
    # New: Enhanced parameters
    if suggestion.parameters:
        print(f"Parameters: {suggestion.parameters}")

🚀 Advanced Usage

Programmatic Metadata Creation

from funimpute.models import ColumnMetadata

metadata = [
    ColumnMetadata(
        column_name="product_code",
        data_type="string",
        max_length=10,
        allowed_values="A1,A2,B1,B2",
        nullable=False,
        description="Product classification code"
    ),
    ColumnMetadata(
        column_name="price",
        data_type="float",
        min_value=0,
        max_value=10000,
        business_rule="Must be non-negative"
    )
]

# Analyze with custom metadata
import pandas as pd
data = pd.read_csv("products.csv")
from funimpute.simple_analyzer import SimpleImputationAnalyzer

analyzer = SimpleImputationAnalyzer()
results = analyzer.analyze_dataframe(data, metadata)

CLI Usage with Enhanced Metadata

# Generate template with new fields
funputer init --data products.csv --output products_metadata.csv

# Edit metadata.csv to add constraints, then:
funputer analyze --data products.csv --metadata products_metadata.csv --output results.json

# View results
funputer report --input results.json --format table

📋 Requirements

• Python: 3.9 or higher
• Dependencies: pandas, numpy, scipy, scikit-learn

🔧 Installation from Source

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

📚 Documentation

• Full API Reference: GitHub Wiki
• Examples: Examples Directory
• Changelog: CHANGELOG.md

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

MIT License - see LICENSE file for details.

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Focus: Get intelligent imputation recommendations with enhanced metadata support, not complex infrastructure.