PUTMCIEBTFPGZIL3LX6AEWJIYCPWIBP5SLUC2QLYQI3HWMJUQCQAC
JGCJ2CQPYNRNLDQGVRIDIB5EGKW2O7PCTAIHKZTTJQXE2VTY27QQC
7ZN6HHL2PQTLSZCD3AHXWAUE5ZGNY4S5PFR2XCDYR35GFGASPDLQC
NMT3JJPPFHOOL37U6JJIUSBCL5C6BRLLH6CDO3TS5YKRDJPK6NFAC
QDEBWR2RIYZNPXOVOSDXPXTXOZ3NVJHRAUDHPCRZXE5DFT2VRCMQC
FCHAPZLDDTY46FYACRWZOPVXYLCSUBPZVNYYGRIUO3IFC6BM2TZAC
4YKXEBAVKKIWUC6EIG4MGSTZOHD7XJSO5O4YNU3OK5SPKMIWX7YQC
2AE4VE7UAQOEXBTOWOSXSEC2Q5PII4PNZH6MZFCLGNY3LGJOGKIAC
QU7MDQED456DFUKHLHCS2SS5QSLDYFU233YNQ27KY2TOVDMTSUKAC
print(time_series.get_year(2011).median_rents())
"""
census_years.append(time_series.get_year(2011))
census_years.append(time_series.get_year(2016))
census_years.append(time_series.get_year(2021))
# Just for checking
for category in range(len(categories)):
print(f"{categories[category]}:")
for _index, (year, states) in enumerate(years.items()):
for _index, (state, data) in enumerate(states.items()):
print(f" {state}: {year}: first: {data[0]}, last: {data[-1]}")
"""
for census_year in census_years:
median_income = find_median(census_year.median_incomes())
median_rent = find_median(census_year.median_rents())
print(median_income, median_rent, round(median_rent / median_income * 100))