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Result format

The result_format parameter may be either a string or a dictionary which specifies the fields to return in result.

  • For string usage, see result_format values.
  • For dictionary usage, result_format which may include the following keys:
    • result_format: Sets the fields to return in result.
    • partial_unexpected_count: Sets the number of results to include in partial_unexpected_count, if applicable. If set to 0, this will suppress the unexpected counts.

result_format values#

Great Expectations supports four values for result_format: BOOLEAN_ONLY, BASIC, SUMMARY, and COMPLETE. The out-of-the-box default is BASIC. Each successive value includes more detail and so can support different use cases for working with Great Expectations, including interactive exploratory work and automatic validation.

Fields within resultBOOLEAN_ONLYBASICSUMMARYCOMPLETE
element_countnoyesyesyes
missing_countnoyesyesyes
missing_percentnoyesyesyes
details (dictionary)Defined on a per-expectation basis
Fields defined for column_map_expectation type expectations:
unexpected_countnoyesyesyes
unexpected_percentnoyesyesyes
unexpected_percent_nonmissingnoyesyesyes
partial_unexpected_listnoyesyesyes
partial_unexpected_index_listnonoyesyes
partial_unexpected_countsnonoyesyes
unexpected_index_listnononoyes
unexpected_listnononoyes
Fields defined for column_aggregate_expectation type expectations:
observed_valuenoyesyesyes
details (e.g. statistical details)nonoyesyes

Example use cases for different result_format values#

result_format SettingExample use case
BOOLEAN_ONLYAutomatic validation. No result is returned.
BASICExploratory analysis in a notebook.
SUMMARYDetailed exploratory work with follow-on investigation.
COMPLETEDebugging pipelines or developing detailed regression tests.

result_format examples#

Example input:

print(list(my_df.my_var))['A', 'B', 'B', 'C', 'C', 'C', 'D', 'D', 'D', 'D', 'E', 'E', 'E', 'E', 'E', 'F', 'F', 'F', 'F', 'F', 'F', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H']

Example outputs for different values of result_format:

my_df.expect_column_values_to_be_in_set(    "my_var",    ["B", "C", "D", "F", "G", "H"],    result_format={'result_format': 'BOOLEAN_ONLY'}){    'success': False}
my_df.expect_column_values_to_be_in_set(    "my_var",    ["B", "C", "D", "F", "G", "H"],    result_format={'result_format': 'BASIC'}){    'success': False,    'result': {        'unexpected_count': 6,        'unexpected_percent': 0.16666666666666666,        'unexpected_percent_nonmissing': 0.16666666666666666,        'partial_unexpected_list': ['A', 'E', 'E', 'E', 'E', 'E']    }}
expect_column_values_to_match_regex(    "my_column",    "[A-Z][a-z]+",    result_format={'result_format': 'SUMMARY'}){    'success': False,    'result': {        'element_count': 36,        'unexpected_count': 6,        'unexpected_percent': 0.16666666666666666,        'unexpected_percent_nonmissing': 0.16666666666666666,        'missing_count': 0,        'missing_percent': 0.0,        'partial_unexpected_counts': [{'value': 'A', 'count': 1}, {'value': 'E', 'count': 5}],        'partial_unexpected_index_list': [0, 10, 11, 12, 13, 14],        'partial_unexpected_list': ['A', 'E', 'E', 'E', 'E', 'E']    }}
my_df.expect_column_values_to_be_in_set(    "my_var",    ["B", "C", "D", "F", "G", "H"],    result_format={'result_format': 'COMPLETE'}){    'success': False,    'result': {        'unexpected_index_list': [0, 10, 11, 12, 13, 14],        'unexpected_list': ['A', 'E', 'E', 'E', 'E', 'E']    }}

Behavior for BOOLEAN_ONLY#

When the result_format is BOOLEAN_ONLY, no result is returned. The result of evaluating the Expectation is
exclusively returned via the value of the success parameter.

For example:

my_df.expect_column_values_to_be_in_set(    "possible_benefactors",    ["Joe Gargery", "Mrs. Gargery", "Mr. Pumblechook", "Ms. Havisham", "Mr. Jaggers"]    result_format={'result_format': 'BOOLEAN_ONLY'}){    'success': False}
my_df.expect_column_values_to_be_in_set(    "possible_benefactors",    ["Joe Gargery", "Mrs. Gargery", "Mr. Pumblechook", "Ms. Havisham", "Mr. Jaggers", "Mr. Magwitch"]    result_format={'result_format': 'BOOLEAN_ONLY'}){    'success': False}

Behavior for BASIC#

A result is generated with a basic justification for why an expectation was met or not. The format is intended for quick, at-a-glance feedback. For example, it tends to work well in Jupyter Notebooks.

Great Expectations has standard behavior for support for describing the results of column_map_expectation and column_aggregate_expectation expectations.

column_map_expectation applies a boolean test function to each element within a column, and so returns a list of
unexpected values to justify the expectation result.

The basic result includes:

{    "success" : Boolean,    "result" : {        "partial_unexpected_list" : [A list of up to 20 values that violate the expectation]        "unexpected_count" : The total count of unexpected values in the column        "unexpected_percent" : The overall percent of unexpected values        "unexpected_percent_nonmissing" : The percent of unexpected values, excluding missing values from the denominator    }}

Note: When unexpected values are duplicated, unexpected_list will contain multiple copies of the value.

[1,2,2,3,3,3,None,None,None,None]
expect_column_values_to_be_unique
{    "success" : Boolean,    "result" : {        "partial_unexpected_list" : [2,2,3,3,3]        "unexpected_count" : 5,        "unexpected_percent" : 0.5,        "unexpected_percent_nonmissing" : 0.8333333    }}

column_aggregate_expectation computes a single aggregate value for the column, and so returns a single observed_value to justify the expectation result.

The basic result includes:

{    "success" : Boolean,    "result" : {        "observed_value" : The aggregate statistic computed for the column    }}

For example:

[1, 1, 2, 2]
expect_column_mean_to_be_between
{    "success" : Boolean,    "result" : {        "observed_value" : 1.5    }}

Behavior for SUMMARY#

A result is generated with a summary justification for why an expectation was met or not. The format is intended
for more detailed exploratory work and includes additional information beyond what is included by BASIC. For example, it can support generating dashboard results of whether a set of expectations are being met.

Great Expectations has standard behavior for support for describing the results of column_map_expectation and column_aggregate_expectation expectations.

column_map_expectation applies a boolean test function to each element within a column, and so returns a list of
unexpected values to justify the expectation result.

The summary result includes:

{    'success': False,    'result': {        'element_count': The total number of values in the column        'unexpected_count': The total count of unexpected values in the column (also in `BASIC`)        'unexpected_percent': The overall percent of unexpected values (also in `BASIC`)        'unexpected_percent_nonmissing': The percent of unexpected values, excluding missing values from the denominator (also in `BASIC`)        "partial_unexpected_list" : [A list of up to 20 values that violate the expectation] (also in `BASIC`)        'missing_count': The number of missing values in the column        'missing_percent': The total percent of missing values in the column        'partial_unexpected_counts': [{A list of objects with value and counts, showing the number of times each of the unexpected values occurs}]        'partial_unexpected_index_list': [A list of up to 20 of the indices of the unexpected values in the column]    }}

For example:

{    'success': False,    'result': {        'element_count': 36,        'unexpected_count': 6,        'unexpected_percent': 0.16666666666666666,        'unexpected_percent_nonmissing': 0.16666666666666666,        'missing_count': 0,        'missing_percent': 0.0,        'partial_unexpected_counts': [{'value': 'A', 'count': 1}, {'value': 'E', 'count': 5}],        'partial_unexpected_index_list': [0, 10, 11, 12, 13, 14],        'partial_unexpected_list': ['A', 'E', 'E', 'E', 'E', 'E']    }}

column_aggregate_expectation computes a single aggregate value for the column, and so returns a observed_value to justify the expectation result. It also includes additional information regarding observed values and counts, depending on the specific expectation.

The summary result includes:

{    'success': False,    'result': {        'observed_value': The aggregate statistic computed for the column (also in `BASIC`)        'element_count': The total number of values in the column        'missing_count':  The number of missing values in the column        'missing_percent': The total percent of missing values in the column        'details': {<expectation-specific result justification fields>}    }}

For example:

[1, 1, 2, 2, NaN]
expect_column_mean_to_be_between
{    "success" : Boolean,    "result" : {        "observed_value" : 1.5,        'element_count': 5,        'missing_count': 1,        'missing_percent': 0.2    }}

Behavior for COMPLETE#

A result is generated with all available justification for why an expectation was met or not. The format is
intended for debugging pipelines or developing detailed regression tests.

Great Expectations has standard behavior for support for describing the results of column_map_expectation and column_aggregate_expectation expectations.

column_map_expectation applies a boolean test function to each element within a column, and so returns a list of unexpected values to justify the expectation result.

The complete result includes:

{    'success': False,    'result': {        "unexpected_list" : [A list of all values that violate the expectation]        'unexpected_index_list': [A list of the indices of the unexpected values in the column]        'element_count': The total number of values in the column (also in `SUMMARY`)        'unexpected_count': The total count of unexpected values in the column (also in `SUMMARY`)        'unexpected_percent': The overall percent of unexpected values (also in `SUMMARY`)        'unexpected_percent_nonmissing': The percent of unexpected values, excluding missing values from the denominator (also in `SUMMARY`)        'missing_count': The number of missing values in the column  (also in `SUMMARY`)        'missing_percent': The total percent of missing values in the column  (also in `SUMMARY`)    }}

For example:

{    'success': False,    'result': {        'element_count': 36,        'unexpected_count': 6,        'unexpected_percent': 0.16666666666666666,        'unexpected_percent_nonmissing': 0.16666666666666666,        'missing_count': 0,        'missing_percent': 0.0,        'unexpected_index_list': [0, 10, 11, 12, 13, 14],        'unexpected_list': ['A', 'E', 'E', 'E', 'E', 'E']    }}

column_aggregate_expectation computes a single aggregate value for the column, and so returns a observed_value to justify the expectation result. It also includes additional information regarding observed values and counts,
depending on the specific expectation.

The complete result includes:

{    'success': False,    'result': {        'observed_value': The aggregate statistic computed for the column (also in `SUMMARY`)        'element_count': The total number of values in the column (also in `SUMMARY`)        'missing_count':  The number of missing values in the column (also in `SUMMARY`)        'missing_percent': The total percent of missing values in the column (also in `SUMMARY`)        'details': {<expectation-specific result justification fields, which may be more detailed than in `SUMMARY`>}    }}

For example:

[1, 1, 2, 2, NaN]
expect_column_mean_to_be_between
{    "success" : Boolean,    "result" : {        "observed_value" : 1.5,        'element_count': 5,        'missing_count': 1,        'missing_percent': 0.2    }}