def extrapolagg(self, categories, npp, geog_code, year_range):
"""
Extrapolate and then aggregate
"""
data = self.extrapolate(npp, geog_code, year_range)
# invert categories (they're the ones to aggregate, not preserve)
return data.groupby(utils.check_and_invert(categories))["OBS_VALUE"].sum().reset_index()
def aggregate(self, categories, variant_name, geog, years=None, ages=range(0,91), genders=[1,2]):
"""
Subset and aggregate the raw data
"""
data = self.detail(variant_name, geog, years, ages, genders)
return data.groupby(utils.check_and_invert(categories))["OBS_VALUE"].sum().reset_index()
def aggregate(self,
categories,
geog_codes,
years=None,
ages=range(0, 91),
genders=[1, 2]):
data = self.filter(geog_codes, years, ages, genders)
# invert categories (they're the ones to aggregate, not preserve)
return data.groupby(utils.check_and_invert(
categories))["OBS_VALUE"].sum().reset_index()
