data-validation-first

What problem does it solve?

Before writing any analysis code, ensure your data is clean, consistent, and trustworthy by performing a repeatable validation checklist that highlights shapes, data types, missing values, and basic statistics.

Core Features & Use Cases

• Data health checks: shape, dtypes, null counts, duplicates, and sample rows to quickly assess data quality.
• Guardrails for modeling: catch common data quality issues before joins, aggregations, or feature engineering.
• Use case: validate a tabular dataset prior to building a regression model to prevent skewed results from bad data.

Quick Start

Run a quick data health check on your DataFrame by inspecting shape, dtypes, nulls, basic statistics, and a sample of rows.