def __build_columns(dataset, dframe, functions, name, no_index):
columns = []
for function in functions:
column = dframe.apply(function, axis=1, args=(dataset, ))
column.name = make_unique(name, [c.name for c in columns])
if no_index:
column = column.reset_index(drop=True)
columns.append(column)
return columns
def __build_columns(dataset, dframe, functions, name, no_index):
columns = []
for function in functions:
column = dframe.apply(function, axis=1, args=(dataset,))
column.name = make_unique(name, [c.name for c in columns])
if no_index:
column = column.reset_index(drop=True)
columns.append(column)
return columns
def __check_name_and_make_unique(self, name, dataset):
"""Check that the name is valid and make unique if valid.
:param name: The name to make unique.
:param dataset: The dataset to make unique for.
:raises: `UniqueCalculationError` if not unique.
:returns: A unique name.
"""
current_names = dataset.labels
if name in current_names:
raise UniqueCalculationError(name, current_names)
return make_unique(name, dataset.schema.keys())
def _check_name_and_make_unique(name, dataset):
"""Check that the name is valid and make unique if valid.
:param name: The name to make unique.
:param dataset: The dataset to make unique for.
:raises: `UniqueCalculationError` if not unique.
:returns: A unique name.
"""
current_names = dataset.labels
if name in current_names:
raise UniqueCalculationError(name, current_names)
return make_unique(name, dataset.schema.keys())
def parse_columns(self, formula, name, dframe=None):
"""Parse formula into function and variables."""
if dframe is None:
dframe = self.dataset.dframe()
functions = self.parser.parse_formula(formula)
columns = []
for function in functions:
column = dframe.apply(
function, axis=1, args=(self.parser.context, ))
column.name = make_unique(name, [c.name for c in columns])
columns.append(column)
return columns
def save(self, dataset, formula, name, group_str=None):
"""Parse, save, and calculate a formula.
Validate `formula` and `group_str` for the given `dataset`. If the
formula and group are valid for the dataset, then save a new
calculation for them under `name`. Finally, create a background task
to compute the calculation.
Calculations are initially saved in a **pending** state, after the
calculation has finished processing it will be in a **ready** state.
:param dataset: The DataSet to save.
:param formula: The formula to save.
:param name: The name of the formula.
:param group_str: Columns to group on.
:type group_str: String, list or strings, or None.
:raises: `ParseError` if an invalid formula was supplied.
"""
calculator = Calculator(dataset)
# ensure that the formula is parsable
groups = self.split_groups(group_str) if group_str else []
aggregation = calculator.validate(formula, groups)
if aggregation:
# set group if aggregation and group unset
if not group_str:
group_str = ''
else:
# ensure the name is unique
name = make_unique(name, dataset.labels + dataset.schema.keys())
record = {
DATASET_ID: dataset.dataset_id,
self.AGGREGATION: aggregation,
self.FORMULA: formula,
self.GROUP: group_str,
self.NAME: name,
self.STATE: self.STATE_PENDING,
}
super(self.__class__, self).save(record)
call_async(calculate_task, self, dataset)
return record
