# unionai-oss/pandera

A light-weight, flexible, and expressive statistical data testing library

Repository: https://github.com/unionai-oss/pandera
Canonical: https://ross.abutalabs.com/products/pandera
Homepage: https://www.union.ai/pandera
Language: Python
License: MIT
License Family: permissive
Topics: pandas, validation, schema, dataframes, testing, pandas-validation, pandas-dataframe, data-validation, data-cleaning, data-check, testing-tools, assertions, data-assertions, data-verification, dataframe-schema, hypothesis-testing, pandas-validator, data-processing
Last push: 2026-08-26T03:06:59+00:00

## Health v2 (maintenance only)
Score: 96/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 90, longevity 100
- inputs: {"age_days": 2863, "days_push": 7, "days_rel": 65, "gap_med": 20, "n_releases_24m": 22}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4442, forks 434 (observed 2026-08-28T04:08:49.418921+00:00)

## What it is
Pandera is a Python library for statistical data validation of dataframe-like objects, supporting pandas, polars, PySpark, and others. It lets users define expressive schemas and validate data at runtime, including testing data transformation functions with hypothesis-based strategies.

## Use cases
- validate pandas dataframes against a schema
- check data quality in ETL pipelines
- test data transformation functions in CI
- assert column types and value constraints on dataframes
- clean and verify messy real-world datasets
- generate synthetic test data from dataframe schemas

## When to choose
- you need schema-based validation for pandas, polars, or pyspark dataframes
- you want runtime type checking of functions that consume or produce dataframes
- you need statistical checks like ranges, categories, or distributions on your data
- you want to catch low-quality data before it reaches ML models or production pipelines

## When to avoid
- you need validation for non-tabular data like JSON documents or raw files
- you want a full data quality dashboard or monitoring UI rather than a programmatic library
- you work outside the Python ecosystem

## Facets
- artifact type: library
- maturity: stable
- function: testing, data-science, etl, form-handling
- domain: data-science, machine-learning, developer-tools
- platform: python
- tags: dataframes, pandas, polars, pyspark, schema-validation, data-quality, hypothesis-testing, statistical-typing, data-engineering

## Member repositories
- unionai-oss/pandera (main) score 96

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:49.418921+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:20:49.775014+00:00, confidence not recorded.
  - readme: https://github.com/unionai-oss/pandera (fetched 2026-08-28T04:08:49.418921+00:00, sha 799a924b41b0)
  - homepage: https://www.union.ai/pandera (fetched 2026-08-29T09:08:03.722454+00:00, sha c3fa3b87264f)
  - site_page: https://www.union.ai/docs (fetched 2026-08-29T09:08:03.736550+00:00, sha 8e997d26c088)
  - site_page: https://www.union.ai/docs/v2/byoc/release-notes (fetched 2026-08-29T09:08:03.739714+00:00, sha d319430e13fa)
  - site_page: https://www.union.ai/support (fetched 2026-08-29T09:08:03.732178+00:00, sha ab942d67aaea)
  - site_page: https://www.union.ai/integrations (fetched 2026-08-29T09:08:03.734503+00:00, sha ecc6f3c326e7)
  - site_page: https://www.union.ai/pricing (fetched 2026-08-29T09:08:03.738140+00:00, sha 85fa762410da)
  - site_page: https://www.union.ai/company (fetched 2026-08-29T09:08:03.741349+00:00, sha 360acfb98d26)
- Data as of 2026-08-30T08:39:29.467469+00:00.