datatidy 0.1.0

A powerful, configuration-driven data processing and cleaning package

Configuration-Driven Data Processing Made Simple

DataTidy

A powerful, configuration-driven data processing and cleaning package for Python with robust fallback capabilities. DataTidy allows you to define complex data transformations, validations, and cleanings through simple YAML configuration files, ensuring 100% reliability in production environments.

🚀 Key Features

• 🔧 Configuration-Driven: Define all transformations in YAML - no code required
• 📊 Multiple Data Sources: CSV, Excel, databases (PostgreSQL, MySQL, Snowflake, etc.)
• 🔗 Multi-Input Joins: Combine data from multiple sources with flexible join operations
• ⚡ Advanced Operations: Map/reduce/filter with lambda functions and chained operations
• 🧠 Dependency Resolution: Automatic execution order planning for complex transformations
• 📈 Time Series Support: Lag operations and rolling window calculations
• 🛡️ Safe Expressions: Secure evaluation with whitelist-based security
• 🎯 Data Validation: Comprehensive validation rules with detailed error reporting
• ⚙️ CLI Interface: Easy-to-use command-line tools for batch processing

🔄 Enhanced Fallback System (v0.1.0)

• 🛡️ 100% Reliability: Dashboard never fails to load data with automatic fallback mechanisms
• ⚖️ Graceful Degradation: Gets sophisticated transformations when possible, basic data when needed
• 🔍 Enhanced Error Logging: Detailed error categorization with actionable debugging suggestions
• 📊 Data Quality Metrics: Compare DataTidy results with fallback data for quality assessment
• 🎛️ Multiple Processing Modes: Strict, partial, and fallback modes for different reliability requirements
• 🔧 Partial Processing: Skip problematic columns while processing successful ones
• 📋 Processing Recommendations: Get specific suggestions for improving configurations

Installation

pip install datatidy

For development installation:

git clone https://github.com/your-repo/datatidy.git
cd datatidy
pip install -e ".[dev]"

Quick Start

1. Create a sample configuration

datatidy sample config.yaml

2. Process your data

datatidy process config.yaml -i input.csv -o output.csv

3. Or use programmatically

from datatidy import DataTidy

# Initialize with configuration
dt = DataTidy('config.yaml')

# Standard processing
result = dt.process_data('input.csv')

# Enhanced processing with fallback
result = dt.process_data_with_fallback('input.csv')

# Save result
dt.process_and_save('output.csv', 'input.csv')

Configuration Structure

DataTidy uses YAML configuration files to define data processing pipelines:

input:
  type: csv                    # csv, excel, database
  source: "data/input.csv"     # file path or SQL query
  options:
    encoding: utf-8
    delimiter: ","

output:
  columns:
    user_id:
      source: "id"             # Source column name
      type: int                # Data type conversion
      validation:
        required: true
        min_value: 1
    
    full_name:
      source: "name"
      type: string
      transformation: "str.title()"  # Python expression
      validation:
        required: true
        min_length: 2
        max_length: 100
    
    age_group:
      transformation: "'adult' if age >= 18 else 'minor'"
      type: string
      validation:
        allowed_values: ["adult", "minor"]

  filters:
    - condition: "age >= 0"
      action: keep

  sort:
    - column: user_id
      ascending: true

global_settings:
  ignore_errors: false
  max_errors: 100
  
  # Enhanced fallback settings
  processing_mode: partial           # strict, partial, or fallback
  enable_partial_processing: true
  enable_fallback: true
  max_column_failures: 5
  failure_threshold: 0.3             # 30% failure rate triggers fallback
  
  # Fallback transformations for problematic columns
  fallback_transformations:
    age_group:
      type: default_value
      value: "unknown"

Examples

Basic CSV Processing

from datatidy import DataTidy

config = {
    "input": {
        "type": "csv",
        "source": "users.csv"
    },
    "output": {
        "columns": {
            "clean_name": {
                "source": "name",
                "transformation": "str.strip().title()",
                "type": "string"
            },
            "age_category": {
                "transformation": "'senior' if age > 65 else ('adult' if age >= 18 else 'minor')",
                "type": "string"
            }
        }
    }
}

dt = DataTidy()
dt.load_config(config)
result = dt.process_data()
print(result)

Database Processing

input:
  type: database
  source: 
    query: "SELECT * FROM users WHERE active = true"
    connection_string: "postgresql://user:pass@localhost/db"

output:
  columns:
    user_email:
      source: "email"
      type: string
      validation:
        pattern: "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
    
    signup_date:
      source: "created_at"
      type: datetime
      format: "%Y-%m-%d"

Excel Processing with Complex Transformations

input:
  type: excel
  source:
    path: "sales_data.xlsx"
    sheet_name: "Q1_Sales"
    options:
      header: 0
      skiprows: 2

output:
  columns:
    revenue_category:
      transformation: |
        'high' if revenue > 100000 else (
          'medium' if revenue > 50000 else 'low'
        )
      validation:
        allowed_values: ["high", "medium", "low"]
    
    formatted_date:
      source: "sale_date"
      type: datetime
      format: "%Y-%m-%d"
    
    clean_product_name:
      source: "product"
      transformation: "str.strip().upper().replace('_', ' ')"
      validation:
        min_length: 1
        max_length: 50

  filters:
    - condition: "revenue > 0"
      action: keep
    - condition: "product != 'DELETED'"
      action: keep

Enhanced Fallback Processing

Production-Ready Data Processing

from datatidy import DataTidy

# Initialize with fallback-enabled configuration
dt = DataTidy('config.yaml')

# Define fallback database query
def fallback_database_query():
    return pd.read_sql("SELECT * FROM facilities", db_connection)

# Process with guaranteed results
result = dt.process_data_with_fallback(
    data=input_df,
    fallback_query_func=fallback_database_query
)

# Your application always gets data!
if result.fallback_used:
    logger.warning("DataTidy processing failed, using database fallback")

# Check processing results
summary = dt.get_processing_summary()
print(f"Success: {summary['success']}")
print(f"Successful columns: {summary['successful_columns']}")
print(f"Failed columns: {summary['failed_columns']}")

# Get improvement recommendations
recommendations = dt.get_processing_recommendations()
for rec in recommendations:
    print(f"💡 {rec}")

# Compare data quality when both available
if not result.fallback_used:
    fallback_data = fallback_database_query()
    quality = dt.compare_with_fallback(fallback_data)
    print(f"Overall quality score: {quality.overall_quality_score:.2f}")

Data Quality Monitoring

from datatidy.fallback.metrics import DataQualityMetrics

# Compare processing results
comparison = DataQualityMetrics.compare_results(
    datatidy_df=processed_data,
    fallback_df=fallback_data,
    datatidy_time=2.3,
    fallback_time=0.8
)

# Print detailed comparison
DataQualityMetrics.print_comparison_summary(comparison)

# Export for analysis
DataQualityMetrics.export_comparison_report(
    comparison, 
    'quality_report.json'
)

Command Line Usage

Enhanced Processing Modes

# Strict mode (default) - fails on any error
datatidy process config.yaml --mode strict

# Partial mode - skip problematic columns
datatidy process config.yaml --mode partial --show-summary

# Fallback mode - use fallback transformations
datatidy process config.yaml --mode fallback

# Development mode with detailed feedback
datatidy process config.yaml --mode partial \\
  --show-summary \\
  --show-recommendations \\
  --error-log debug.json

Process Data

# Basic processing
datatidy process config.yaml

# With input/output files
datatidy process config.yaml -i input.csv -o output.csv

# Ignore validation errors
datatidy process config.yaml --ignore-errors

Validate Configuration

datatidy validate config.yaml

Create Sample Configuration

datatidy sample my_config.yaml

Expression System

DataTidy includes a safe expression parser that supports:

Basic Operations

• Arithmetic: +, -, *, /, //, %, **
• Comparison: ==, !=, <, <=, >, >=
• Logical: and, or, not
• Membership: in, not in

Functions

• Type conversion: str(), int(), float(), bool()
• Math: abs(), max(), min(), round()
• String methods: upper(), lower(), strip(), replace(), etc.

Examples

transformations:
  # Conditional expressions
  status: "'active' if last_login_days < 30 else 'inactive'"
  
  # String operations
  clean_name: "name.strip().title()"
  
  # Mathematical calculations
  bmi: "weight / (height / 100) ** 2"
  
  # Complex conditions
  risk_level: |
    'high' if (age > 65 and income < 30000) else (
      'medium' if age > 40 else 'low'
    )

Validation Rules

DataTidy supports comprehensive validation:

validation:
  required: true              # Field must not be null
  nullable: false             # Field cannot be null
  min_value: 0               # Minimum numeric value
  max_value: 100             # Maximum numeric value
  min_length: 2              # Minimum string length
  max_length: 50             # Maximum string length
  pattern: "^[A-Za-z]+$"     # Regex pattern
  allowed_values: ["A", "B"] # Whitelist of values

Error Handling

dt = DataTidy('config.yaml')
result = dt.process_data('input.csv')

# Check for errors
if dt.has_errors():
    for error in dt.get_errors():
        print(f"Error: {error['message']}")

API Reference

DataTidy Class

Core Methods

• load_config(config): Load configuration from file or dict
• process_data(data=None): Process data according to configuration
• process_and_save(output_path, data=None): Process and save data
• get_errors(): Get list of processing errors
• has_errors(): Check if errors occurred

Enhanced Fallback Methods

• process_data_with_fallback(data=None, fallback_query_func=None): Process with fallback capabilities
• get_processing_summary(): Get detailed processing summary with metrics
• get_error_report(): Get categorized error report with debugging info
• get_processing_recommendations(): Get actionable recommendations for improvements
• compare_with_fallback(fallback_df): Compare DataTidy results with fallback data
• export_error_log(file_path): Export detailed error log to JSON
• set_processing_mode(mode): Set processing mode (strict, partial, fallback)

Processing Result Class

Properties

• success: Boolean indicating overall processing success
• data: Processed DataFrame result
• processing_mode: Mode used for processing
• successful_columns: List of successfully processed columns
• failed_columns: List of failed columns
• fallback_used: Boolean indicating if fallback was activated
• processing_time: Time taken for processing
• error_log: Detailed list of processing errors

Data Quality Metrics

Static Methods

• DataQualityMetrics.compare_results(datatidy_df, fallback_df): Compare two DataFrames
• DataQualityMetrics.print_comparison_summary(comparison): Print formatted comparison
• DataQualityMetrics.export_comparison_report(comparison, file_path): Export report to JSON

Configuration Schema

See Configuration Reference for complete schema documentation.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Changelog

Version 0.1.0

• Initial release
• Basic CSV, Excel, and database support
• Safe expression engine
• Comprehensive validation system
• CLI interface