def modify_dataset(ds: MultiRegionDataset) -> MultiRegionDataset:
ts_copy = ds.timeseries.copy()
# Test positivity should be a ratio
ts_copy.loc[:, CommonFields.TEST_POSITIVITY_7D] = (
ts_copy.loc[:, CommonFields.TEST_POSITIVITY_7D] / 100.0)
levels = set(
Region.from_location_id(l).level for l in
ds.timeseries.index.get_level_values(CommonFields.LOCATION_ID))
# Should only be picking up county all_df for now. May need additional logic if states
# are included as well
assert levels == {AggregationLevel.COUNTY}
# Duplicating DC County results as state results because of a downstream
# use of how dc state data is used to override DC county data.
dc_results = ts_copy.xs(DC_COUNTY_LOCATION_ID,
axis=0,
level=CommonFields.LOCATION_ID,
drop_level=False)
dc_results = dc_results.rename(
index={DC_COUNTY_LOCATION_ID: DC_STATE_LOCATION_ID},
level=CommonFields.LOCATION_ID)
ts_copy = ts_copy.append(dc_results, verify_integrity=True).sort_index()
return dataclasses.replace(ds,
timeseries=remove_trailing_zeros(ts_copy),
timeseries_bucketed=None)
def add_state_location_id_index_level(df: pd.DataFrame) -> pd.DataFrame:
"""Returns df with the location_id of the state in a new level STATE_LOCATION_ID."""
state_index = (df.index.get_level_values(
CommonFields.LOCATION_ID).map(lambda loc_id: Region.from_location_id(
loc_id).get_state_region().location_id).rename(STATE_LOCATION_ID))
return df.set_index(state_index, append=True)
