def get_usa_by_county_with_projection_df(input_dir, intervention_type):
us_only = _get_usa_by_county_df()
fips_df = FIPSPopulation.local().data # used to get interventions
interventions_df = _get_interventions_df()
projections_df = get_county_projections_df(input_dir, intervention_type,
interventions_df)
counties_decorated = (us_only.merge(
projections_df,
left_on="State/County FIPS Code",
right_on="FIPS",
how="inner",
).merge(fips_df[["state", "fips"]],
left_on="FIPS",
right_on="fips",
how="inner").merge(interventions_df,
left_on="state",
right_on="state",
how="inner"))
counties_remapped = counties_decorated.rename(
columns=OUTPUT_COLUMN_REMAP_TO_RESULT_DATA)
counties = pd.DataFrame(counties_remapped,
columns=RESULT_DATA_COLUMNS_COUNTIES)
counties = counties.fillna(NULL_VALUE)
counties.index.name = "OBJECTID"
# assert unique key test
if counties["Combined Key"].value_counts().max() != 1:
raise Exception(
f"counties['Combined Key'].value_counts().max() = {counties['Combined Key'].value_counts().max()}, at input_dir {input_dir}."
)
return counties
def get_usa_by_county_with_projection_df(input_dir, intervention_type):
us_only = get_usa_by_county_df()
fips_df = FIPSPopulation.local().data # used to get interventions
interventions_df = get_interventions_df() # used to say what state has what interventions
projections_df = get_county_projections_df(input_dir, intervention_type)
counties_decorated = us_only.merge(
projections_df, left_on='State/County FIPS Code', right_on='FIPS', how='inner'
).merge(
fips_df[['state', 'fips']], left_on='FIPS', right_on='fips', how='inner'
).merge(
interventions_df, left_on='state', right_on='state', how = 'inner'
)
counties_remapped = counties_decorated.rename(columns=OUTPUT_COLUMN_REMAP_TO_RESULT_DATA)
counties = pd.DataFrame(counties_remapped, columns=RESULT_DATA_COLUMNS_COUNTIES)
counties = counties.fillna(NULL_VALUE)
counties.index.name = 'OBJECTID'
# assert unique key test
assert counties['Combined Key'].value_counts().max() == 1
return counties
def get_usa_by_county_with_projection_df(input_dir, intervention_type):
us_only = _get_usa_by_county_df()
fips_df = FIPSPopulation.local().data # used to get interventions
interventions_df = _get_interventions_df()
projections_df = get_county_projections_df(input_dir, intervention_type, interventions_df)
counties_decorated = (
us_only.merge(projections_df, on=CommonFields.FIPS, how="inner")
.merge(fips_df[[CommonFields.STATE, CommonFields.FIPS]], on=CommonFields.FIPS, how="inner",)
.merge(interventions_df, on=CommonFields.STATE, how="inner")
)
counties_remapped = counties_decorated.rename(columns=OUTPUT_COLUMN_REMAP_TO_RESULT_DATA)
counties = pd.DataFrame(counties_remapped)[RESULT_DATA_COLUMNS_COUNTIES]
counties = counties.fillna(NULL_VALUE)
counties.index.name = "OBJECTID"
if counties["Combined Key"].value_counts().max() != 1:
combined_key_max = counties["Combined Key"].value_counts().max()
raise Exception(
"counties['Combined Key'].value_counts().max() = "
f"{combined_key_max}, at input_dir {input_dir}."
)
return counties