great-expectations
Always know what to expect from your data.
Decision gist · record as of 2026-08-14
Yes. Great Expectations is actively maintained, widely used (top 1000 on PyPI), has no known vulnerabilities, and solves a real problem—making data quality testable and auditable. The Apache-2.0 license is permissive. Install friction is low. The dependency footprint is large but standard for data work. Start with it if your team needs to validate data quality systematically or document data contracts.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 through 3.13; experimental support for Python 3.14 and later requires setting GX_PYTHON_EXPERIMENTAL environment variable.
- Low install friction with a pure-Python wheel.
- Active maintenance: last commit 2026-08-13, release 7 days old.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions; suitable for most organizational contexts.
last release 2026-08-07 (7 days) · last repo commit 2026-08-13 · 11,711 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 27,681,262 downloads/mo, #851 on PyPI
Alternatives
Verify before relying
pip install great_expectations
import great_expectations as gx
context = gx.get_context()- Whether the package's data-source integrations require additional system libraries or external services beyond the listed runtime dependencies.
- Performance characteristics when validating large datasets or running many expectations in parallel.
- Extent of backward compatibility guarantees between minor versions.
What it is and what it does
Great Expectations is a data validation and documentation framework that lets teams define Expectations—expressive, unit-test-like assertions about data shape, content, and quality. Rather than writing custom validation code for each pipeline, you declare what your data should look like in a common language, then run those Expectations against actual data to catch quality issues early. The framework automatically generates human-readable documentation of validation results, creating a shared record of data quality checks and their outcomes.
The package is built around a Data Context—a configuration object that manages your Expectations, validation runs, and results. You work with it by importing great_expectations, creating or loading a context, attaching Expectations to your data, and validating. It integrates with standard data tools like pandas and numpy, and supports Python 3.10 through 3.13. The dependency footprint is substantial (18 runtime packages) but overlaps heavily with typical data-science environments, so it often adds little to an existing stack.
Use it for
- Catch data quality regressions in ETL pipelines by validating incoming or transformed data against known-good schemas and distributions.
- Document data assumptions for a team: encode business rules about what valid data looks like so new team members understand the data contract.
- Generate audit trails of data validation for compliance: automatically record which datasets passed or failed which quality checks and when.
- Prevent downstream analytics errors by validating data quality before it reaches dashboards or models.
- Test data migrations: define Expectations for source and target schemas to verify that transformed data meets requirements.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Great Expectations is actively maintained, widely used (top 1000 on PyPI), has no known vulnerabilities, and solves a real problem—making data quality testable and auditable. The Apache-2.0 license is permissive. Install friction is low. The dependency footprint is large but standard for data work. Start with it if your team needs to validate data quality systematically or document data contracts.
Install
great-expectations on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance: last commit 2026-08-13, release 7 days old. Eighteen runtime dependencies including pandas, numpy, and pydantic—a substantial but standard data-science stack.
Requires Python 3.10 through 3.13; experimental support for Python 3.14 and later requires setting GX_PYTHON_EXPERIMENTAL environment variable.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions; suitable for most organizational contexts.
Quickstart
pip install great_expectations
import great_expectations as gx
context = gx.get_context()Verify before relying
- Whether the package's data-source integrations require additional system libraries or external services beyond the listed runtime dependencies.
- Performance characteristics when validating large datasets or running many expectations in parallel.
- Extent of backward compatibility guarantees between minor versions.
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 packagesaltaircryptographyjinja2jsonschemamarshmallowmistunenumpypackagingpandaspydanticpyparsingpython-dateutilrequestsruamel.yamlscipytqdmtyping-extensionstzlocal |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 27,681,262 / month, #851 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | 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 |
Evidence: great_expectations-1.20.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “data quality assertions”
- great-expectationsDefines and validates data quality expectations—unit tests for…
- acryl-great-expectationsGX Core provides data quality testing through Expectations—expressive…
- assertsProvides stand-alone assertion functions for Python that work outside…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also acryl-great-expectations · great-expectations-experimental · airflow-provider-great-expectations · spark-expectations · expects · whylogs · dbt-core · prefect-snowflake · dagster-pandera · schemathesis