v0.10.3

• Added approximate flag into the is_complete implementation for pyspark to run comparisson with pydeequ
• Resolved JOSS issues for documentation and references against other data quality frameworks
• Updated the test/performance folder with recent versions of all frameworks and accurate docker containers for each test

v0.10.2

• Added documentation to main classes Check and Rule
• Changed to base=2 the implementation of has_entropy for pyspark as it does reflect with the common uses

v0.10.1

• Upgrade to duckdb==0.10.2
• Community guidelines in README. Thanks @devarops
• Fix pipeline with new SF account

v0.10.0

• Addition of daft data frame support. Attribution to @dsaad68 👏
• @dsaad68 largest contribution to the project ever! 🏆
• Thanks for covering all: test, docs and code 💯

v0.9.2

• Removal of deprecated sum(axis=1) in polars in favor for sum_horizontal()
• Thanks @StuffbyYuki

v0.9.1

• Added support for spark-connect via SPARK_REMOTE environment variable

v.0.9.0

• Fix an important issue when working with datasets >1 billion rows were violations were present, and status was marked as PASS
• Inclusion of new Controls
• Structure for PDF report added

v0.8.8

• JOSS submission

v0.8.7: Feature pyspark hotfix (#171)

• Hot fix for pyspark on reconciliation of results. It was returning only last rule

v0.8.6

• Added percentage_fill for Control class and pyspark dataframes
• Added percentage_empty for Control class and pyspark dataframes