def jq_filter(value, filter_expression, all=False):
"""
Parse input with jq language.
"""
if not HAS_JQ:
raise AnsibleError(missing_required_lib("jq"))
if all:
return jq.all(filter_expression, value)
else:
return jq.first(filter_expression, value)
def transform_with_jq(data: Any,
jq_filter: str) -> Union[PdDataList, PdDataDict]:
"""Apply a jq filter on data before it's passed to a pd.DataFrame"""
all_data: Union[List[PdDataList], List[PdDataDict],
PdDataList] = jq.all(jq_filter, data)
# If the data is already presented as a list of rows,
# then undo the nesting caused by "multiple_output" jq option
if len(all_data) == 1 and (
isinstance(all_data[0], list) # List[PdDataList]
# detects another valid datastructure [{col1:[value, ...], col2:[value, ...]}]
or (isinstance(all_data[0], dict) and isinstance(
list(all_data[0].values())[0], list)) # List[PdDataDict]
):
return all_data[0]
else:
return cast(PdDataList, all_data)
def all_function_with_json_text_input_returns_all_output_element_in_list(
self):
output = jq.all(".[] + 1", text="[1, 2, 3]")
assert_equal([2, 3, 4], output)
def all_function_with_json_value_input_returns_all_output_elements_in_list(
self):
output = jq.all(".[] + 1", [1, 2, 3])
assert_equal([2, 3, 4], output)