v0.4.0

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