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_expectation`s 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