Datasets:

postalup
/

us-zip-code-rankings-demographics-acs-2023

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'population', 'land_area_sq_miles'}) and 3 missing columns ({'population_2021', 'growth_pct', 'population_2023'}).

This happened while the csv dataset builder was generating data using

hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023/largest_zip_codes_by_area.csv (at revision ce89ea90e0b41a1580eff816fab8831cd9cfdf93), ['hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/fastest_growing_zip_codes.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/largest_zip_codes_by_area.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/poorest_zip_codes.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/richest_zip_codes.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/richest_zip_per_state.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/us_zip_demographics_acs_2023.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              rank: int64
              zip: int64
              city: string
              state: string
              land_area_sq_miles: double
              population: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 958
              to
              {'rank': Value('int64'), 'zip': Value('int64'), 'city': Value('string'), 'state': Value('string'), 'population_2021': Value('int64'), 'population_2023': Value('int64'), 'growth_pct': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'population', 'land_area_sq_miles'}) and 3 missing columns ({'population_2021', 'growth_pct', 'population_2023'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023/largest_zip_codes_by_area.csv (at revision ce89ea90e0b41a1580eff816fab8831cd9cfdf93), ['hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/fastest_growing_zip_codes.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/largest_zip_codes_by_area.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/poorest_zip_codes.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/richest_zip_codes.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/richest_zip_per_state.csv', 'hf://datasets/postalup/us-zip-code-rankings-demographics-acs-2023@ce89ea90e0b41a1580eff816fab8831cd9cfdf93/us_zip_demographics_acs_2023.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

rank int64	zip int64	city string	state string	population_2021 int64	population_2023 int64	growth_pct float64
6,953	33,965	Fort Myers	FL	2,530	4,416	74.5
15,178	78,152	Saint Hedwig	TX	2,380	3,958	66.3
6,421	32,461	Inlet Beach	FL	2,158	3,517	63
6,994	34,211	Bradenton	FL	13,251	20,786	56.9
14,611	76,005	Arlington	TX	3,967	6,165	55.4
13,621	70,803	Baton Rouge	LA	2,222	3,448	55.2
15,990	83,844	Moscow	ID	2,319	3,545	52.9
11,378	55,114	Saint Paul	MN	3,124	4,655	49
15,586	80,019	Aurora	CO	4,162	6,141	47.5
6,212	31,738	Coolidge	GA	2,031	2,960	45.7
14,998	77,441	Fulshear	TX	22,665	32,905	45.2
17,068	92,254	Mecca	CA	7,478	10,779	44.1
6,839	33,620	Tampa	FL	4,743	6,819	43.8
8,035	38,665	Sarah	MS	2,666	3,817	43.2
7,730	37,408	Chattanooga	TN	2,121	3,029	42.8
8,414	40,759	Rockholds	KY	2,254	3,194	41.7
14,568	75,833	Centerville	TX	2,223	3,143	41.4
945	5,405	Burlington	VT	2,015	2,840	40.9
19	652	Garrochales	PR	3,193	4,492	40.7
3,587	19,043	Holmes	PA	2,535	3,550	40
5,995	30,567	Pendergrass	GA	3,691	5,169	40
7,134	34,987	Port Saint Lucie	FL	12,818	17,911	39.7
4,788	26,351	Glenville	WV	3,324	4,635	39.4
17,074	92,274	Thermal	CA	12,843	17,909	39.4
6,182	31,562	Saint George	GA	2,463	3,398	38
16,366	85,933	Overgaard	AZ	2,908	3,989	37.2
16,729	90,089	Los Angeles	CA	3,498	4,777	36.6
11,781	57,706	Ellsworth Afb	SD	2,204	3,009	36.5
6,813	33,576	San Antonio	FL	4,698	6,415	36.5
8,428	41,003	Berry	KY	2,124	2,891	36.1
8,041	38,677	Oxford	MS	3,679	5,006	36.1
425	2,210	Boston	MA	4,830	6,554	35.7
14,637	76,044	Godley	TX	6,466	8,759	35.5
15,215	78,234	Jbsa Ft Sam Houston	TX	4,655	6,281	34.9
7,112	34,773	Saint Cloud	FL	4,074	5,495	34.9
15,321	78,597	South Padre Island	TX	2,138	2,881	34.8
17,382	93,626	Friant	CA	2,255	3,039	34.8
17,434	93,944	Monterey	CA	2,017	2,719	34.8
8,265	39,762	Mississippi State	MS	3,541	4,764	34.5
3,463	18,372	Tannersville	PA	2,532	3,397	34.2
14,757	76,487	Poolville	TX	2,833	3,799	34.1
16,585	89,086	North Las Vegas	NV	6,499	8,714	34.1
17,705	95,245	Mokelumne Hill	CA	2,422	3,227	33.2
7,595	37,060	Eagleville	TN	2,683	3,574	33.2
1,039	6,103	Hartford	CT	2,137	2,845	33.1
15,774	80,927	Colorado Springs	CO	5,231	6,957	33
14,014	73,047	Hinton	OK	4,816	6,402	32.9
13,734	71,454	Montgomery	LA	2,200	2,922	32.8
4,449	23,801	Fort Lee	VA	6,271	8,318	32.6
10,193	48,811	Carson City	MI	5,600	7,416	32.4
1	59,301	Miles City	MT	null	null	null
2	59,538	Malta	MT	null	null	null
3	81,640	Maybell	CO	null	null	null
4	82,190	Yellowstone National Park	WY	null	null	null
5	82,414	Cody	WY	null	null	null
6	82,633	Douglas	WY	null	null	null
7	83,611	Cascade	ID	null	null	null
8	85,634	Sells	AZ	null	null	null
9	85,643	Willcox	AZ	null	null	null
10	87,825	Magdalena	NM	null	null	null
11	88,030	Deming	NM	null	null	null
12	88,201	Roswell	NM	null	null	null
13	89,049	Tonopah	NV	null	null	null
14	89,412	Gerlach	NV	null	null	null
15	89,445	Winnemucca	NV	null	null	null
16	97,910	Jordan Valley	OR	null	null	null
17	99,519	Anchorage	AK	null	null	null
18	99,557	Aniak	AK	null	null	null
19	99,566	Chitina	AK	null	null	null
20	99,573	Copper Center	AK	null	null	null
21	99,574	Cordova	AK	null	null	null
22	99,576	Dillingham	AK	null	null	null
23	99,588	Glennallen	AK	null	null	null
24	99,615	Kodiak	AK	null	null	null
25	99,627	Mc Grath	AK	null	null	null
26	99,635	Nikiski	AK	null	null	null
27	99,640	Nondalton	AK	null	null	null
28	99,664	Seward	AK	null	null	null
29	99,667	Skwentna	AK	null	null	null
30	99,691	Nikolai	AK	null	null	null
31	99,712	Fairbanks	AK	null	null	null
32	99,729	Cantwell	AK	null	null	null
33	99,730	Central	AK	null	null	null
34	99,737	Delta Junction	AK	null	null	null
35	99,738	Eagle	AK	null	null	null
36	99,739	Elim	AK	null	null	null
37	99,740	Fort Yukon	AK	null	null	null
38	99,743	Healy	AK	null	null	null
39	99,752	Kotzebue	AK	null	null	null
40	99,760	Nenana	AK	null	null	null
41	99,768	Ruby	AK	null	null	null
42	99,772	Shishmaref	AK	null	null	null
43	99,780	Tok	AK	null	null	null
44	99,781	Venetie	AK	null	null	null
45	99,801	Juneau	AK	null	null	null
46	99,827	Haines	AK	null	null	null
47	99,833	Petersburg	AK	null	null	null
48	99,835	Sitka	AK	null	null	null
49	99,921	Craig	AK	null	null	null
50	99,929	Wrangell	AK	null	null	null

End of preview.

US ZIP Code Rankings & Demographics (Census ACS 2023)

Clean, ready-to-use rankings and demographics for US ZIP codes, derived from the US Census Bureau American Community Survey (2019–2023 5-year estimates) and USPS ZIP→city/state mapping.

Maintained by PostalUp — US postal & address data. Live, always-current versions of every ranking below:

Richest ZIP codes → https://postalup.com/richest-zip-codes
Poorest ZIP codes → https://postalup.com/poorest-zip-codes
Largest ZIP codes by area → https://postalup.com/largest-zip-codes
Fastest-growing ZIP codes → https://postalup.com/fastest-growing-zip-codes
ZIP codes by income → https://postalup.com/zip-codes-by-income
ZIP codes by population → https://postalup.com/zip-codes-by-population

Files

File	Rows	What it is
`us_zip_demographics_acs_2023.csv`	~33,772	Full ACS 2023 demographics per ZIP (ZCTA)
`richest_zip_codes.csv`	50	Highest median household income
`poorest_zip_codes.csv`	50	Lowest median household income (+ poverty rate)
`largest_zip_codes_by_area.csv`	50	Largest land area (sq mi)
`fastest_growing_zip_codes.csv`	50	Fastest 2021→2023 population growth
`richest_zip_per_state.csv`	52	Highest-income ZIP in each state/territory

Methodology & honest caveats

Source: US Census Bureau ACS 2019–2023 5-year estimates (ZCTA level) + USPS city/state. ZIP Code Tabulation Areas (ZCTAs) approximate USPS ZIP codes.
Income is top-coded. The ACS caps published median household income at $250,001 — any ZIP above that reads 250001 (income_top_coded = true in richest_zip_codes.csv). We do not invent figures above the cap; tied ZIPs are ordered by bachelor's-degree rate, then home-ownership rate, then population.
Suppressed income excluded. Some ZCTAs report a 0 / unavailable median income (data not published, not a real $0). The poorest ranking excludes these and requires ≥ 1,000 households; poverty rate is shown for corroboration.
Growth is the change between overlapping ACS 5-year estimates (2021 vs
1. — a smoothed multi-year trend, not a single-year rate. Minimum base population 2,000 (small bases inflate percentages).

License

CC BY 4.0. Free to use, including commercially — attribution required: credit PostalUp (https://postalup.com). Need a commercial-license file with USPS-enriched ZIP+4, monthly refreshes, and more formats (CSV/JSON/SQL/Parquet)? See https://postalup.com/downloads.

Downloads last month: 38

Total file size:

2.85 MB