def _cython_agg_general(self, how):
label_list = [ping.labels for ping in self.groupings]
shape = self._group_shape
# TODO: address inefficiencies, like duplicating effort (should
# aggregate all the columns at once?)
output = {}
cannot_agg = []
for name, obj in self._iterate_slices():
if issubclass(obj.dtype.type, (np.number, np.bool_)):
if obj.dtype != np.float64:
obj = obj.astype('f8')
else:
cannot_agg.append(name)
continue
result, counts = lib.group_aggregate(obj, label_list,
shape, how=how)
result = result.ravel()
mask = counts.ravel() > 0
output[name] = result[mask]
if len(output) == 0:
raise GroupByError('No numeric types to aggregate')
return self._wrap_aggregated_output(output, mask)
def _cython_agg_general(self, how):
label_list = [ping.labels for ping in self.groupings]
shape = self._group_shape
# TODO: address inefficiencies, like duplicating effort (should
# aggregate all the columns at once?)
output = {}
cannot_agg = []
for name, obj in self._iterate_slices():
try:
obj = np.asarray(obj, dtype=float)
except ValueError:
cannot_agg.append(name)
continue
result, counts = _tseries.group_aggregate(obj, label_list,
shape, how=how)
result = result.ravel()
mask = counts.ravel() > 0
output[name] = result[mask]
return self._wrap_aggregated_output(output, mask)