Validate Polars data frames with dy.Schema and dy.Collection.
npx skills add https://github.com/Quantco/dataframely --skill dataframely
Please help me install this Agent Skill. Skill: dataframely Source: https://github.com/Quantco/dataframely/tree/main/skills Command: npx skills add https://github.com/Quantco/dataframely --skill dataframely
This guide helps engineers apply dataframely to enforce schema-driven validation in Polars workflows, reducing data quality issues and streamlining typing and testing.
Read this guide to implement dataframely schemas and validations in your Polars workflows.
High-intent search queries and answers about installing and using this skill.
Schema validation in Polars uses dy.Schema and dy.Collection to model dataframe structures, applying .validate() and .filter() methods to enforce constraints and catch data quality issues at runtime.
You enforce schema contracts in Polars ETL pipelines by defining dy.Schema structures with type hints, then using .validate() to check data frames and .filter() to remove records violating constraints.
The best way to filter invalid rows is using the dy.Schema.filter method, which evaluates dataframe records against your defined schema constraints and separates valid data from invalid entries.
Yes, you can model multiple related dataframe structures by defining a dy.Collection, which groups multiple dy.Schema definitions to validate complex data relationships across your Polars workflows.
Schema-driven validation works for testing Polars workflows by leveraging type hints and testing conventions alongside dy.Schema, improving readability and reliability when validating dataframe transformations.
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