1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
- pandas-profiling
- pandas-dataframe
- statistics
- jupyter-notebook
Repository Index
47,133 repositories indexed
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Always know what to expect from your data.
- pipeline-tests
- dataquality
- datacleaning
- datacleaner
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
- weak-supervision
- data-cleaning
- data-quality
- noisy-labels
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
- data-drift
- jupyter-notebook
- pandas-dataframe
- machine-learning
A light-weight, flexible, and expressive statistical data testing library
- pandas
- validation
- schema
- dataframes
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
- machine-learning
- ml
- model-validation
- data-validation
Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.
- dataprep
- data-science
- datapreparation
- dataconnector
Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.
- analytics
- bigquery
- data-modeling
- data-pipelines
Library for exploring and validating machine learning data
TranslatedOntiCards is an enterprise-oriented AI Data Hub that offers one-stop capabilities around business databases: data source connection, metadata understanding, natural language query, cross-source fusion, data quality inspection, query auditing, data asset management, and more
pyspark methods to enhance developer productivity 📣 👯 🎉
Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!
Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match
Data validation toolkit for assessing and monitoring data quality.
- data-validation
- tabular-data
- data-testing
- data-quality
The repository host gives no description.
- hacktoberfest
- data
- datalineage
- dbt
Field-level assurance for data pipelines: mapping-driven source-to-target validation, fault-injection test scoring and explainable risk-adaptive test prioritization.
Data validator agains Splunk Common Information Model (CIM)
Lightweight DataFrame validation decorators for Pandas, Polars, Modin, and PyArrow. No custom types required.
Declarative data quality engine. Define checks in YAML, run anywhere.
- airflow
- anomaly-detection
- data-contracts
- data-quality
A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.
- anomaly-detection
- bigquery
- cli
- data-engineering
Blazingly fast data comparison tool for Python, powered by Rust. Compare massive CSV/Parquet datasets instantly.
Validates tabular CSV data using predefined validations, inspired from its Python homologue "Great Expectations".
- common-lisp
- csv
- data-validation
- great-expectations
Open, audit-grade agentic data quality framework with portable industry packs