great_expectations.expectations.core.expect_column_values_to_not_be_in_set

Module Contents

Classes

ExpectColumnValuesToNotBeInSet(configuration: Optional[ExpectationConfiguration] = None)

Expect column entries to not be in the set.

class great_expectations.expectations.core.expect_column_values_to_not_be_in_set.ExpectColumnValuesToNotBeInSet(configuration: Optional[ExpectationConfiguration] = None)

Bases: great_expectations.expectations.expectation.ColumnMapExpectation

Expect column entries to not be in the set.

For example:

# my_df.my_col = [1,2,2,3,3,3]
>>> my_df.expect_column_values_to_not_be_in_set(
    "my_col",
    [1,2]
)
{
  "success": false
  "result": {
    "unexpected_count": 3
    "unexpected_percent": 50.0,
    "unexpected_percent_nonmissing": 50.0,
    "partial_unexpected_list": [
      1, 2, 2
    ],
  },
}

expect_column_values_to_not_be_in_set is a column_map_expectation.

Parameters
  • column (str) – The column name.

  • value_set (set-like) – A set of objects used for comparison.

Keyword Arguments

mostly (None or a float between 0 and 1) – Return “success”: True if at least mostly fraction of values match the expectation. For more detail, see mostly.

Other Parameters
  • result_format (str or None) – Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see result_format.

  • include_config (boolean) – If True, then include the expectation config as part of the result object. For more detail, see include_config.

  • catch_exceptions (boolean or None) – If True, then catch exceptions and include them as part of the result object. For more detail, see catch_exceptions.

  • meta (dict or None) – A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see meta.

Returns

An ExpectationSuiteValidationResult

Exact fields vary depending on the values passed to result_format and include_config, catch_exceptions, and meta.

See also

expect_column_values_to_be_in_set

library_metadata
map_metric = column_values.not_in_set
success_keys = ['value_set', 'mostly', 'parse_strings_as_datetimes']
default_kwarg_values
validate_configuration(self, configuration: Optional[ExpectationConfiguration])
classmethod _prescriptive_renderer(cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs)
_pandas_column_values_not_in_set(self, series: pd.Series, metrics: Dict, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, runtime_configuration: dict = None, filter_column_isnull: bool = True)