def json_config_expectation(name, file_path):
def _file_passes(_info, df):
with open(file_path) as ff:
expt_config = json.load(ff)
# This is necessary because ge ends up coercing a type change
# on the same dataframe instance, changing the type. But a
# ge can't be copied, because it causes an error.
# The error is
# AttributeError: 'PandasDataset' object has no attribute
# 'discard_subset_failing_expectations'
# See https://github.com/great-expectations/great_expectations/issues/342
df_copy = copy.deepcopy(df)
ge_df = ge.from_pandas(df_copy, expectations_config=expt_config)
validate_result = ge_df.validate()
check.invariant('success' in validate_result)
check.invariant('results' in validate_result)
return ExpectationResult(
success=validate_result['success'],
metadata_entries=[
EventMetadataEntry.json(label='result',
data=validate_result)
],
)
return ExpectationDefinition(name, expectation_fn=_file_passes)
def ge_expectation(name, ge_callback):
def _do_expectation(_info, df):
ge_df = ge.from_pandas(df)
ge_result = ge_callback(ge_df)
check.invariant('success' in ge_result)
return ExpectationResult(success=ge_result['success'],
result_context=ge_result)
return ExpectationDefinition(name, expectation_fn=_do_expectation)
def ge_expectation(name, ge_callback):
def _do_expectation(_info, df):
ge_df = ge.from_pandas(df)
ge_result = ge_callback(ge_df)
check.invariant('success' in ge_result)
return ExpectationResult(
success=ge_result['success'],
metadata_entries=[
EventMetadataEntry.json(label='result', data=ge_result)
],
)
return ExpectationDefinition(name, expectation_fn=_do_expectation)