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A language for filtering, matching, and validating Python dictionaries
The main features of ambitioninc/kmatch are: Data Validation.
Projects with overlapping indexed features include: alecthomas/voluptuous — CONTRIBUTIONS ONLY: Voluptuous, despite the name, is a Python data validation library. capitalone/datacompy — Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more! cleanlab/cleanlab — Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting… colinhacks/zod — Zod is a TypeScript-first schema declaration and validation library designed to ensure end-to-end data integrity. It… deepchecks/deepchecks — Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality… ajv-validator/ajv — Ajv is a high-performance data validation framework that compiles JSON schemas into optimized, standalone JavaScript…
CONTRIBUTIONS ONLY: Voluptuous, despite the name, is a Python data validation library.
Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!
Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting label errors and increasing overall dataset quality. It implements a confident learning framework that iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples. The project provides specialized utilities for active learning optimization, allowing for the selection of the most impactful examples for labeling or re-labeling. It also includes an outlier detection tool to identify atypical data poin
Ajv is a high-performance data validation framework that compiles JSON schemas into optimized, standalone JavaScript functions. By transforming declarative schema definitions into executable code, it eliminates runtime interpretation overhead and provides a secure, efficient way to enforce data integrity across both browser and server environments. The library distinguishes itself through its focus on performance and type safety. It employs advanced compilation techniques, including abstract syntax tree optimization and function caching, to ensure rapid validation. Beyond standard checks, it