def METHOD_NAME(
    ephemeral_context_with_defaults,
):
    context = ephemeral_context_with_defaults
    datasource = context.sources.add_or_update_pandas("my_datasource")
    asset = datasource.add_dataframe_asset("my_asset")
    # noinspection PyTypeChecker
    df = pd.DataFrame(
        {
            "non-null": [i for i in range(100)],
            "null": [None for _ in range(100)],
            "low-null": [None for _ in range(38)] + [i for i in range(62)],
        }
    )
    batch_request = asset.build_batch_request(dataframe=df)
    with patch(
        "great_expectations.rule_based_profiler.data_assistant.data_assistant.run_profiler_on_data"
    ) as mock_run_profiler_on_data:
        context.assistants.missingness.run(batch_request=batch_request)
    call_args = mock_run_profiler_on_data.call_args[1]
    profiler = call_args["profiler"]
    rules = profiler.config.rules
    expectation_configuration_builders = rules["column_value_missing_rule"][
        "expectation_configuration_builders"
    ]
    for builder in expectation_configuration_builders:
        validation_parameter_builder_configs = builder[
            "validation_parameter_builder_configs"
        ]
        assert len(validation_parameter_builder_configs) == 1
        assert validation_parameter_builder_configs[0]["mode"] == "single_batch"