datawash 0.2.2

Intelligent data cleaning and quality analysis

DataWash

Intelligent data cleaning and quality analysis for Python

Installation • Quick Start • Features • Documentation • Examples

---

DataWash analyzes your tabular data, detects quality issues, suggests prioritized fixes, and generates reproducible Python code — all in a few lines of code.

from datawash import analyze

report = analyze("messy_data.csv")
print(f"Quality Score: {report.quality_score}/100")
clean_df = report.apply_all()
print(report.generate_code())

Why DataWash?

Problem	DataWash Solution
Missing values silently break ML models	Automatic detection + smart filling strategies
Inconsistent date formats cause parsing errors	Detects and standardizes to ISO format
Duplicate rows inflate statistics	Identifies and removes exact duplicates
Boolean values stored as "yes"/"no" strings	Converts to proper boolean type
Manual data cleaning is tedious and error-prone	Generates reproducible Python code

Installation

pip install datawash

Optional extras:

pip install datawash[formats]  # Parquet + Excel support
pip install datawash[ml]       # ML-powered detection (coming soon)
pip install datawash[all]      # All optional dependencies
pip install datawash[dev]      # Development tools

Quick Start

Python API

from datawash import analyze

# 1. Analyze your data (sampling enabled by default for large datasets)
report = analyze("data.csv")  # or pass a DataFrame

# 2. Check quality score
print(f"Quality Score: {report.quality_score}/100")
print(f"Issues Found: {len(report.issues)}")

# 3. Review suggestions
for s in report.suggestions:
    print(f"[{s.id}] {s.action}")

# 4. Apply all fixes
clean_df = report.apply_all()

# 5. Or apply selectively
clean_df = report.apply([1, 3, 5])  # by suggestion ID

# 6. Generate reproducible code
print(report.generate_code())

# Disable sampling for exact results on large datasets
report = analyze("data.csv", sample=False)

# Disable parallel processing
report = analyze("data.csv", parallel=False)

Command Line

# Analyze and see quality report
datawash analyze data.csv

# Get prioritized suggestions
datawash suggest data.csv --use-case ml

# Clean and export
datawash clean data.csv -o clean.csv --apply-all

# Generate Python code
datawash codegen data.csv --apply-all

Features

Data Quality Detection

Detector	What It Finds
Missing	Null values, empty strings, whitespace-only values
Duplicates	Exact duplicate rows
Formats	Mixed case, inconsistent dates, whitespace padding
Outliers	Statistical anomalies (IQR or Z-score)
Types	Numbers/booleans stored as strings
Similarity	Potentially duplicate columns

Smart Transformations

Transformer	Operations
Missing	Drop rows, fill with median/mode/value, clean empty strings
Duplicates	Remove exact duplicates
Types	Convert to numeric, boolean, datetime
Formats	Standardize case, dates, strip whitespace
Columns	Drop, rename, merge columns
Categories	Normalize categorical values

Intelligent Suggestion System

• Conflict Resolution: Automatically prevents conflicting transformations
• Execution Ordering: Applies fixes in optimal order (6 phases)
• Use-Case Aware: Priorities adjust for ML, analytics, or export workflows
• Contextual Rationale: Every suggestion explains why it's recommended

Code Generation

# Generate a reusable cleaning function
code = report.generate_code(style="function")

# Or a standalone script
code = report.generate_code(style="script")

Performance

DataWash v0.2.0 is optimized for large datasets:

Dataset	Time	Throughput
1M rows x 10 cols	0.72s	1.4M rows/sec
100K rows x 50 cols	2.13s	47K rows/sec
10K rows x 100 cols	4.35s	2.3K rows/sec
1M rows x 50 cols	3.24s	309K rows/sec
50K rows x 250 cols	9.99s	5K rows/sec

Optimizations include:

• Smart sampling for datasets >=50K rows (10-20x speedup)
• Parallel column profiling and detection
• 31% memory reduction via dtype optimization
• O(n) similarity detection with MinHash + LSH

Examples

We provide ready-to-run examples in the examples/ directory:

Example	Description
quickstart.py	Basic workflow: analyze → suggest → apply → codegen
csv_cleaning.py	Load CSV, clean, save with CLI equivalents
ml_preprocessing.py	ML-optimized cleaning workflow
jupyter_demo.ipynb	Interactive notebook with visualizations

Sample datasets in examples/sample_data/:

• customers_messy.csv - Names, emails, phones with various issues
• orders_messy.csv - Dates, amounts, categories with inconsistencies
• employees_messy.csv - Mixed types, duplicates, outliers

# Run an example
python examples/quickstart.py

Documentation

Document	Description
Getting Started	Installation and first steps
User Guide	Complete feature walkthrough
API Reference	Detailed API documentation
CLI Reference	Command-line interface guide
Configuration	Customization options
Contributing	How to contribute

Use Cases

Choose a use case to get optimized suggestions:

report = analyze(df, use_case="ml")  # or "general", "analytics", "export"

Use Case	Prioritizes
general	Balanced approach for exploration
ml	Duplicates, missing values, type conversions
analytics	Consistency, date formats, outliers
export	Format standardization, clean values

Configuration

report = analyze(
    "data.csv",
    use_case="ml",
    config={
        "detectors": {
            "outlier_method": "zscore",  # or "iqr"
            "outlier_threshold": 2.5,
            "min_similarity": 0.8,
        },
        "suggestions": {
            "max_suggestions": 20,
        },
    },
)

Project Status

Metric	Value
Source Code	~2,900 lines
Test Code	~1,270 lines
Tests	114 passing
Coverage	~92%
Python	3.10, 3.11, 3.12
Platforms	Linux, macOS, Windows

What's Working

• ✅ Multi-format loading (CSV, JSON, Parquet, Excel)
• ✅ Comprehensive profiling and statistics
• ✅ 6 detectors for common data quality issues
• ✅ 6 transformers with multiple operations each
• ✅ Smart suggestion system with conflict resolution
• ✅ Reproducible Python code generation
• ✅ Rich CLI with colored output
• ✅ Jupyter notebook support

What's Next

• ML-powered semantic similarity detection
• Fuzzy duplicate detection for near-duplicate rows
• Advanced imputation (KNN, MICE)
• Cloud storage connectors (S3, BigQuery)
• PII detection for sensitive data
• Schema validation for expected column types and constraints

Requirements

• Python >= 3.10
• Core: pandas, numpy, pydantic, rich, typer, scikit-learn
• Optional: pyarrow (Parquet), openpyxl (Excel)

Development

# Clone and install
git clone https://github.com/Pranav1011/DataWash.git
cd DataWash
pip install -e ".[dev,all]"

# Run tests
pytest

# Format code
black src tests
ruff check src tests

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

Areas where help is needed:

• ML module implementation (sentence-transformers)
• Additional detectors (PII, schema validation)
• Performance optimization
• Documentation and examples
• Cloud connectors

License

MIT License - see LICENSE for details.

Acknowledgments

Built with pandas, pydantic, rich, typer, and scikit-learn.