Stop Writing .isnull() — Audit Your Dataset in One Line with OMR

A developer built OMR (Omni Data Refinement), an open-source Python library that replaces dozens of lines of boilerplate data quality checks with a single call. The library provides a health score, auto-cleaning, schema validation, drift detection, and statistical analysis for datasets. OMR aims to be the first tool run after loading data, offering a quality layer on top of pandas.

Every time I start a new data project, I write the same boilerplate: python df.isnull .sum df.duplicated .sum df.dtypes df.describe df df 'age' < 0 ... 40 more lines After doing this for the hundredth time, I built OMR Omni Data Refinement — a Python library that replaces all of that with a single call. What is OMR? OMR is an open-source Python framework for dataset quality, validation, profiling, and monitoring. Think of it as a health check for your data — before you do any ML, EDA, or transformation, you should know how healthy your data actually is. Installation bash pip install omni-data-refinement Only needs pandas, numpy, and rich. No cloud, no LLMs. The Core Feature: Health Score python import pandas as pd from omr import Dataset df = pd.read csv "your data.csv" report = Dataset df .health print report.score e.g. 87/100 You get a 0-100 quality score covering 5 dimensions: Pillar What it checks Completeness Missing values Uniqueness Duplicates Consistency Type mismatches Validity Value ranges and format rules Conformity Schema adherence Auto-Cleaning python dataset = Dataset df dataset.clean dataset.explain changes See exactly what was fixed OMR auto-resolves missing values, duplicates, and type mismatches — and gives you a full transformation log so nothing is a black box. Schema Validation python from omr import schemas schema = { "age": schemas.PositiveInteger max=120 , "salary": schemas.PositiveFloat min=10000 , "status": schemas.OneOf "active", "inactive" , "email": schemas.Email } dataset.validate schema Drift Detection Compare your current dataset against production or a previous version: python prod dataset = Dataset pd.read csv "prod data.csv" dataset.compare prod dataset Uses PSI, KS Test, and JS Divergence under the hood. Statistical Analysis python dataset.analyze Detects: outliers, multicollinearity, skewness, class imbalance, zero variance Column Profiling replaces .describe python dataset.profile Unlike .describe which only covers numeric columns, OMR profiles every column — numeric, categorical, and boolean — in a clean terminal table. The Fluent API Chain everything together: python clean df = Dataset df .health .clean .analyze .export Exports HTML, Markdown, or JSON report Why I Built This The goal is for OMR to become the first thing you run after pd.read csv — not a replacement for Pandas, but the quality layer on top of it. Links PyPI: pip install omni-data-refinement GitHub: https://github.com/Omar-Alshafai2/omni-data-refinement Docs: https://Omar-Alshafai2.github.io/omni-data-refinement/ Examples: https://Omar-Alshafai2.github.io/omni-data-refinement/examples/ What data quality problems do you run into most? I would love to know what to build next.