Welcome to Great Expectations version 0.4.0! Please note that this release includes several major breaking API changes. Please see the changelog below for more information!
v.0.4.0
- Initial implementation of data context API and SqlAlchemyDataset including implementations of the following expectations:
- expect_column_to_exist
- expect_table_row_count_to_be
- expect_table_row_count_to_be_between
- expect_column_values_to_not_be_null
- expect_column_values_to_be_null
- expect_column_values_to_be_in_set
- expect_column_values_to_be_between
- expect_column_mean_to_be
- expect_column_min_to_be
- expect_column_max_to_be
- expect_column_sum_to_be
- expect_column_unique_value_count_to_be_between
- expect_column_proportion_of_unique_values_to_be_between
- Major refactor of output_format to new result_format parameter. See docs for full details.
- exception_list and related uses of the term exception have been renamed to unexpected
- the output formats are explicitly hierarchical now, with BOOLEAN_ONLY < BASIC < SUMMARY < COMPLETE.
column_aggregate_expectations now return element count and related information included at the BASIC level or higher.
- New expectation available for parameterized distributions--expect_column_parameterized_distribution_ks_test_p_value_to_be_greater_than (what a name! :) -- (thanks @ccnobbli)
- ge.from_pandas() utility (thanks @schrockn)
- Pandas operations on a PandasDataset now return another PandasDataset (thanks @dlwhite5)
- expect_column_to_exist now takes a column_index parameter to specify column order (thanks @louispotok)
- Top-level validate option (ge.validate())
- ge.read_json() helper (thanks @rjurney)
- Behind-the-scenes improvements to testing framework to ensure parity across data contexts.
- Documentation improvements, bug-fixes, and internal api improvements