Standard arguments for Expectations

All Expectations return a JSON-serializable dictionary when evaluated, and share four standard (optional) arguments:

• result_format: Controls what information is returned from the evaluation of the Expectation.
• catch_exceptions: If true, execution will not fail if the Expectation encounters an error. Instead, it will return success = False and provide an informative error message.
• meta: Allows user-supplied meta-data to be stored with an Expectation.

All ColumnMapExpectations and MultiColumnMapExpectation also have the following argument:

• mostly: A special argument that allows for fuzzy validation based on some percentage (available for all column_map_expectations and multicolumn_map_expectations)

result_format

See Result format for more information.

catch_exceptions

All Expectations accept a boolean catch_exceptions parameter. If this parameter is set to True, then Great Expectations will intercept any exceptions so that execution will not fail if the Expectation encounters an error. Instead, if Great Excpectations catches an exception while evaluating an Expectation, the Expectation result will ( in BASIC and SUMMARY modes) return the following informative error message:

Python

{
    "result": False,
    "catch_exceptions": True,
    "exception_traceback": "..."
}

catch_exceptions is on by default in command-line validation mode, and off by default in exploration mode.

meta

All Expectations accept an optional meta parameter. If meta is a valid JSON-serializable dictionary, it will be
passed through to the expectation_result object without modification. The meta parameter can be used to add
helpful markdown annotations to Expectations (shown below). These Expectation "notes" are rendered within
Expectation Suite pages in Data Docs.

Python

validator.expect_column_values_to_be_in_set(
    "my_column",
    ["a", "b", "c"],
    meta={
      "notes": {
        "format": "markdown",
        "content": [
          "#### These are expectation notes \n - you can use markdown \n - or just strings"
        ]
      }
    }
)
# This returns:
{
    "success": False,
    "meta": {
      "notes": {
        "format": "markdown",
        "content": [
          "#### These are expectation notes \n - you can use markdown \n - or just strings"
        ]
      }
    }
}

mostly

mostly is a special argument that is automatically available in all column_map_expectations and multicolumn_map_expectations. mostly must be a float between 0 and 1. Great Expectations evaluates it as a percentage, allowing some wiggle room when evaluating Expectations: as long as mostly percent of rows evaluate to True, the Expectation returns "success": True.

Python

[0,1,2,3,4,5,6,7,8,9]

validator.expect_column_values_to_be_between(
    "my_column",
    min_value=0,
    max_value=7
)
# This returns:
{
    "success": False,
    ...
}

validator.expect_column_values_to_be_between(
    "my_column",
    min_value=0,
    max_value=7,
    mostly=0.7
)
# This returns:
{
    "success": True,
    ...
}

Expectations with mostly return exception lists even if they succeed:

Python

validator.expect_column_values_to_be_between(
    "my_column",
    min_value=0,
    max_value=7,
    mostly=0.7
)
# This returns:
{
  "success": true
  "result": {
    "unexpected_percent": 0.2,
    "partial_unexpected_index_list": [
      8,
      9
    ],
    "partial_unexpected_list": [
      8,
      9
    ],
    "unexpected_percent_nonmissing": 0.2,
    "unexpected_count": 2
  }
}

Checkpoints and result_format

While result_format and catch_expectations are both standard arguments for Expectations, the result_format argument is also a valid parameter when included in calls to the run(...) command of a Checkpoint.

Reminder:

For more detailed information on how to define result_format values, please see our reference guide on result_format.