{"categories":[{"label":"Quality Assurance","url":"https://skillfed.io/packages/category/software-development-quality-assurance/3"}],"enrichment":{"capability":"Dataframely validates the schema and content of Polars data frames using declarative class-based schemas with field constraints and custom validation rules.","skillfed_tags":["polars-native","schema-validation","data-quality"],"use_cases":["Validate incoming CSV or database exports against an expected schema before processing in a data pipeline.","Enforce business rules (e.g., price > 0, bedroom-to-bathroom ratios) on data frame columns using declarative cross-field rules.","Add type hints to data frames in notebooks or scripts so that IDE and type checkers understand the structure of validated data.","Catch and report schema violations (missing columns, wrong types, null values) early in ETL workflows.","Define reusable schemas for repeated validation tasks across multiple data sources or team members."],"what_it_does":"Dataframely is a Python validation library designed specifically for Polars data frames. It lets you define schemas as Python classes with typed fields and optional constraints (nullable, min_length, etc.), then validate and cast data frames against those schemas in a single call. The library supports both field-level validation and cross-field rules written as Polars expressions, making it possible to enforce complex business logic (e.g., ratio constraints between columns) declaratively.\n\nThe package depends on numpy, polars, and typing-extensions. It targets modern Python versions (3.10+) and ships as compiled wheels for multiple platforms (macOS, Linux, Windows). Validation is typically used in data pipelines to catch schema mismatches early and ensure data quality before downstream processing.","worth_installing":"Yes, with conditions. Dataframely is actively maintained, has no known vulnerabilities, and solves a real problem for Polars users who need declarative schema validation. However, verify the license before use in proprietary work, and be aware that the package is relatively new (first release March 2025)\u2014test it in non-critical pipelines first if you require battle-tested stability."},"id":"dataframely","links":{"html":"https://skillfed.io/packages/dataframely","md":"https://skillfed.io/packages/dataframely.md","pypi":"https://pypi.org/project/dataframely/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-31","license_spdx":null,"license_treatment":"unclear","name":"dataframely","python_support":"supports_current","summary":"A declarative, polars-native data frame validation library"},"popularity":{"monthly_downloads":297366,"position":7886,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.0.0"}