def METHOD_NAME(self, breakdown, df):
    demo_col = std_col.RACE_CATEGORY_ID_COL if breakdown == RACE else breakdown
    unknown = Race.UNKNOWN.value if breakdown == RACE else UNKNOWN
    df = df.rename(columns={'demographic_category': demo_col})
    demo_rows = set(BREAKDOWN_MAP[breakdown].keys())
    df = df.loc[df[demo_col].isin(demo_rows)].reset_index(drop=True)
    df = df.replace(BREAKDOWN_MAP[breakdown])
    known_df = df.loc[df[demo_col] != unknown].reset_index(drop=True)
    unknown_df = df.loc[df[demo_col] == unknown].reset_index(drop=True)
    known_df = known_df.rename(columns={'administered_dose1_pct_known': std_col.VACCINATED_PCT_SHARE})
    unknown_df = unknown_df.rename(columns={'administered_dose1_pct_us': std_col.VACCINATED_PCT_SHARE})
    df = pd.concat([known_df, unknown_df])
    df[std_col.VACCINATED_PER_100K] = df['administered_dose1_pct'].apply(calc_per_100k)
    df.loc[df[demo_col].isin(ALLS), std_col.VACCINATED_PCT_SHARE] = 100.0
    if breakdown == AGE:
        df[std_col.VACCINATED_POP_PCT] = df[demo_col].map(AGE_GROUPS_TO_POP_PCT)
    else:
        df = merge_pop_numbers(df, breakdown, NATIONAL_LEVEL)
        df = df.rename(columns={std_col.POPULATION_PCT_COL: std_col.VACCINATED_POP_PCT})
    df[std_col.STATE_FIPS_COL] = US_FIPS
    df[std_col.STATE_NAME_COL] = US_NAME
    df = df[[std_col.STATE_NAME_COL, std_col.STATE_FIPS_COL, demo_col,
             std_col.VACCINATED_PCT_SHARE, std_col.VACCINATED_POP_PCT,
             std_col.VACCINATED_PER_100K]]
    if breakdown == RACE:
        std_col.add_race_columns_from_category_id(df)
    return df