Projects sharing features with Datacompy

NumPy and Pandas interface to Big Data

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

Zod is a TypeScript-first schema declaration and validation library designed to ensure end-to-end data integrity. It functions as a runtime type guard, allowing developers to define complex data structures through a declarative, chainable syntax. By using these schema definitions, the library automatically derives static TypeScript types, eliminating the need for manual type duplication and ensuring that runtime data matches expected application contracts. The library distinguishes itself through functional schema composition, which enables the creation of hierarchical structures by nesting a

Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality suite and performance evaluator designed to verify the integrity and performance of models and datasets from research through production. The project functions as a model monitoring tool for tracking data drift and performance degradation in production environments. It allows for the creation of custom validation suites and utilizes a pluggable check architecture to automate quality checks within continuous integration pipelines. The framework covers a broad range of capabil

Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine learning models and large language models. It functions as a monitoring tool for detecting data drift and quality degradation in tabular datasets, while providing a specialized analyzer for the faithfulness and correctness of retrieval augmented generation systems. The project distinguishes itself through an evaluation framework that utilizes judge models and custom rubrics to score language model outputs. It includes tools for iterative prompt optimization and the generation of

Modeling domain data on the basis of Clojure records.

Efficient, hassle-free function call validation with a concise inline syntax for clojure.spec and Malli

Structural validation library for Clojure(Script)

Your team of research agents. Or give researchers to your AI.

Blazingly fast data comparison tool for Python, powered by Rust. Compare massive CSV/Parquet datasets instantly.

Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match

Superstruct is a JavaScript and TypeScript data validation library used to verify that data structures match defined shapes and types. It functions as a composable schema builder and a TypeScript schema validator, ensuring that runtime data checks remain synchronized with static type definitions. The library features a data coercion engine that transforms input values or injects default values before the validation process is executed. It enables the creation of complex validation rules by nesting, merging, or omitting properties from existing structures. Its capabilities cover the validatio

Complete instrumentation for clojure.spec

Yup is a JavaScript schema validation library used to define data shapes and validate runtime values. It functions as an object schema validator and a data coercion engine, allowing developers to transform raw input values into desired types before performing validation checks. The library is distinguished by its support for dynamic schema validation, where rules can be adjusted at runtime based on sibling field values or external context. It also enables recursive data structuring for polymorphic fields and provides a system for extracting static TypeScript interfaces from runtime schema def

This project is a Python implementation of the JSON Schema specification, providing a library for verifying that data instances conform to defined schemas. It serves as a data validation framework capable of validating both the data itself and the schemas against official meta-schemas to ensure structural correctness. The library features a schema reference resolver that maps URIs to definitions, enabling the resolution of internal and remote references for modular schema management. It is designed for extensibility, allowing for the definition of custom keywords, custom type-checking logic,

Schema validation just got Pythonic

A validation DSL for Clojure & Clojurescript applications

A JSONSchema validator that uses code generation to be extremely fast

A simple validation library for Clojure and ClojureScript

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

High-performance data-driven data specification library for Clojure/Script.

Tools for exploratory data analysis in Python

:whitecheckmark: Easy property validation for JavaScript, Node and Express.

Clojure(Script) library for declarative data description and validation

Lightweight data validation and adaptation Python library.