Development of datamend Python Library

v0.1.0 is live on PyPI · 113 tests passing

datamend is the production-grade Python library that automatically repairs corrupt data, enforces statistical contracts, detects distribution drift, and traces model failures — in one unified API, zero configuration.

Quick Start

datamend's top-level API is designed to be learned in minutes and relied on for years.

Four Pillars

Each pillar is a standalone powerhouse. Together, they give you complete visibility and control over your data — from raw ingestion all the way to model output attribution.

How It Works

MendPipeline chains all four pillars in a stateful object — fit once on training data, run on every production batch in milliseconds.

Documentation

Complete API reference, tutorials, algorithm explanations, and real-world patterns — all in one place.

Performance

Benchmarked on a 100,000-row × 20-column dataset · MacBook Pro M2 · Python 3.11 · average of 5 runs

datamend's FailureTrace is 7× faster than SHAP on the same dataset because it uses surrogate importances rather than marginal Shapley value computation. Benchmarks are indicative and vary by hardware, dataset shape, and Python version.

Comparison

Each alternative solves one slice of the problem. datamend solves all four — in one install, one API, one report. No stitching five libraries together.

Integrations

datamend integrates with the tools data scientists already use — experiment trackers, version control systems, and CI/CD pipelines.