great-expectations

Always know what to expect from your data.

great-expectations Permissive license Apache-2.0 Active ★ 11,710 v1.20.0 released 2026-08-07

Install

great-expectations on PyPI

pip

pip install great-expectations

uv

uv add great-expectations

poetry

poetry add great-expectations

Package facts

License	Apache-2.0 (permissive)
Python support	supports the current Python release (<3.14,>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	18 — altair, cryptography, jinja2, jsonschema, marshmallow, mistune, numpy, packaging, pandas, pydantic, pyparsing, python-dateutil, requests, ruamel.yaml, scipy, tqdm, typing-extensions, tzlocal
Maintenance	actively maintained — 6 days since the last release
Last repo commit	2026-08-13
First released	2017-12-22
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: great_expectations-1.20.0-py3-none-any.whl

Keywords: data, science, testing, pipeline, data, quality, dataquality, validation, datavalidation

Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Other AudienceIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/EngineeringTopic :: Software DevelopmentTopic :: Software Development :: Testing

About great-expectations

from the package's own PyPI description — quoted content, verbatim

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AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

Great Expectations is a data validation and quality framework that lets you define, test, and document data expectations as expressive unit tests, then automatically generate validation reports.

Low friction installation with a pure-wheel distribution. Actively maintained with a release 6 days ago and strong community engagement. Supports Python 3.10–3.13 with experimental 3.14+ support via environment variable.

Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution in both open-source and commercial projects with minimal restrictions.

Usage

pip install great_expectations

import great_expectations as gx
context = gx.get_context()

Requires Python 3.10 or later; GX recommends running within a virtual environment.

Verdict: Great Expectations is a mature, actively maintained data validation framework with no known vulnerabilities. Its permissive Apache-2.0 license and low install friction make it suitable for production data quality workflows. The 18 runtime dependencies are well-established libraries (pandas, numpy, pydantic, jsonschema, etc.), supporting common data science and validation tasks.

Needs verification

Whether the 18 runtime dependencies introduce any transitive security concerns or version conflicts in specific deployment environments.
Performance characteristics and memory footprint when validating very large datasets or running many concurrent expectations.
Actual GitHub star count and repository activity metrics beyond the last commit date.

data validation frameworkdata quality testingexpectations unit testsdata pipeline validationautomated data quality checksdata documentation generationschema validation python