def geo_reindex(self, data):
"""
Reindex dataframe based on desired output geography.
Args:
data: dataframe, the output of load_data::load_data()
Returns:
reindexed dataframe
"""
geo_map = GeoMapper()
if self.geo == "county":
data_frame = geo_map.fips_to_megacounty(
data,
Config.MIN_DEN,
Config.MAX_BACKWARDS_PAD_LENGTH,
thr_col="den",
mega_col=self.geo)
elif self.geo == "state":
data_frame = geo_map.replace_geocode(data,
from_code="fips",
new_col=self.geo,
new_code="state_id")
data_frame[self.geo] = data_frame[self.geo]
elif self.geo in ["msa", "hhs", "nation"]:
data_frame = geo_map.replace_geocode(data,
from_code="fips",
new_code=self.geo)
elif self.geo == "hrr":
data_frame = data # data is already adjusted in aggregation step above
else:
logging.error(
"%s is invalid, pick one of 'county', 'state', 'msa', 'hrr', 'hhs', nation'",
self.geo)
return False
unique_geo_ids = pd.unique(data_frame[self.geo])
data_frame.set_index([self.geo, 'date'], inplace=True)
# for each location, fill in all missing dates with 0 values
multiindex = pd.MultiIndex.from_product(
(unique_geo_ids, self.fit_dates), names=[self.geo, "date"])
assert (
len(multiindex) <=
(GeoConstants.MAX_GEO[self.geo] * len(self.fit_dates))
), "more loc-date pairs than maximum number of geographies x number of dates"
# fill dataframe with missing dates using 0
data_frame = data_frame.reindex(multiindex, fill_value=0)
data_frame.fillna(0, inplace=True)
return data_frame
def geo_reindex(self, data):
"""Reindex based on geography, include all date, geo pairs.
Args:
data: dataframe, the output of loadcombineddata
Returns:
dataframe
"""
# get right geography
geo = self.geo
gmpr = GeoMapper()
if geo not in {"county", "state", "msa", "hrr", "nation", "hhs"}:
logging.error("{0} is invalid, pick one of 'county', "
"'state', 'msa', 'hrr', 'hss','nation'".format(geo))
return False
if geo == "county":
data_frame = gmpr.fips_to_megacounty(data,
Config.MIN_DEN,
Config.MAX_BACKFILL_WINDOW,
thr_col="den",
mega_col=geo)
elif geo == "state":
data_frame = gmpr.replace_geocode(data,
"fips",
"state_id",
new_col="state")
else:
data_frame = gmpr.replace_geocode(data, "fips", geo)
unique_geo_ids = pd.unique(data_frame[geo])
data_frame.set_index([geo, Config.DATE_COL], inplace=True)
# for each location, fill in all missing dates with 0 values
multiindex = pd.MultiIndex.from_product(
(unique_geo_ids, self.fit_dates), names=[geo, Config.DATE_COL])
assert (
len(multiindex) <=
(len(gmpr.get_geo_values(gmpr.as_mapper_name(geo))) *
len(self.fit_dates))
), "more loc-date pairs than maximum number of geographies x number of dates"
# fill dataframe with missing dates using 0
data_frame = data_frame.reindex(multiindex, fill_value=0)
data_frame.fillna(0, inplace=True)
return data_frame