Great Expectations

Great Expectations

Great Expectations (GX) is the leading open-source Python library for data quality. It lets you define "expectations" — assertions about your data — and automatically generates human-readable documentation and data quality reports.

Key Features

• Expectation Suite: 300+ built-in expectations (column types, ranges, uniqueness, regex, statistical distributions)
• Data Docs: Auto-generated HTML reports showing expectation results with data samples
• Checkpoints: Integrate validation into Airflow, Prefect, Dagster, dbt, or any CI/CD pipeline
• Multi-backend: Validate data in Pandas, Spark, Snowflake, BigQuery, Redshift, Databricks
• Custom expectations: Extend with Python for domain-specific rules
• Profiling: Auto-generate an initial Expectation Suite from a data sample

Quick Start

pip install great_expectations
gx init

import great_expectations as gx

context = gx.get_context()
batch = context.sources.pandas_default.read_csv("my_data.csv")

batch.expect_column_values_to_not_be_null("user_id")
batch.expect_column_values_to_be_between("age", min_value=0, max_value=120)
batch.expect_column_values_to_match_regex("email", r".+@.+\..+")

results = batch.validate()
print(results.success)

Add to ai-supply

npx ai-supply add great-expectations-data-quality

Curated mirror of the open-source Great Expectations (Apache-2.0). Get it from the source.