def _profile(cls, dataset, configuration=None):
logger.debug(f"Running profiler with configuration: {configuration}")
if configuration == "demo":
return cls._demo_profile(dataset)
existing_columns = dataset.get_table_columns()
selected_columns = existing_columns
included_expectations = []
excluded_expectations = []
if configuration:
if ("included_expectations" in configuration
and "excluded_expectations" in configuration):
raise ProfilerError(
"Please specify either `included_expectations` or `excluded_expectations`."
)
if "included_expectations" in configuration:
included_expectations = configuration["included_expectations"]
if included_expectations in [False, None, []]:
included_expectations = None
_check_that_expectations_are_available(dataset,
included_expectations)
if "excluded_expectations" in configuration:
excluded_expectations = configuration["excluded_expectations"]
_check_that_expectations_are_available(dataset,
excluded_expectations)
if ("included_columns" in configuration
and "excluded_columns" in configuration):
raise ProfilerError(
"Please specify either `excluded_columns` or `included_columns`."
)
elif "included_columns" in configuration:
selected_columns = configuration["included_columns"]
elif "excluded_columns" in configuration:
excluded_columns = configuration["excluded_columns"]
if excluded_columns in [False, None, []]:
excluded_columns = []
selected_columns = set(existing_columns) - set(
excluded_columns)
_check_that_columns_exist(dataset, selected_columns)
if included_expectations is None:
suite = cls._build_column_description_metadata(dataset)
# remove column exist expectations
suite.expectations = []
return suite
dataset.set_default_expectation_argument("catch_exceptions", False)
dataset = cls._build_table_row_count_expectation(dataset)
dataset.set_config_value("interactive_evaluation", True)
dataset = cls._build_table_column_expectations(dataset)
column_cache = {}
if selected_columns:
for column in selected_columns:
cardinality = cls._get_column_cardinality_with_caching(
dataset, column, column_cache)
column_type = cls._get_column_type_with_caching(
dataset, column, column_cache)
if cardinality in [
ProfilerCardinality.TWO,
ProfilerCardinality.VERY_FEW,
ProfilerCardinality.FEW,
]:
cls._create_expectations_for_low_card_column(
dataset, column, column_cache)
elif cardinality in [
ProfilerCardinality.MANY,
ProfilerCardinality.VERY_MANY,
ProfilerCardinality.UNIQUE,
]:
# TODO we will want to finesse the number and types of
# expectations created here. The simple version is blacklisting
# and the more complex version is desired per column type and
# cardinality. This deserves more thought on configuration.
dataset.expect_column_values_to_be_unique(column)
if column_type in [
ProfilerDataType.INT, ProfilerDataType.FLOAT
]:
cls._create_expectations_for_numeric_column(
dataset, column)
elif column_type in [ProfilerDataType.DATETIME]:
cls._create_expectations_for_datetime_column(
dataset, column)
elif column_type in [ProfilerDataType.STRING]:
cls._create_expectations_for_string_column(
dataset, column)
elif column_type in [ProfilerDataType.UNKNOWN]:
logger.debug(
f"Skipping expectation creation for column {column} of unknown type: {column_type}"
)
if excluded_expectations:
dataset = _remove_table_expectations(dataset,
excluded_expectations)
dataset = _remove_column_expectations(dataset,
excluded_expectations)
if included_expectations:
for expectation in dataset.get_expectation_suite(
discard_failed_expectations=False).expectations:
if expectation.expectation_type not in included_expectations:
try:
dataset.remove_expectation(
expectation_type=expectation.expectation_type,
expectation_kwargs=expectation.kwargs,
column=expectation.kwargs.get("column", None),
remove_multiple_matches=True,
)
except ValueError:
logger.debug(
f"Attempted to remove {expectation}, which was not found."
)
expectation_suite = cls._build_column_description_metadata(dataset)
return expectation_suite
