Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
url: string
bytes: int64
sha256: string
retrieved_utc: string
headers: struct<Content-Length: string, Last-Modified: string>
child 0, Content-Length: string
child 1, Last-Modified: string
members: list<item: struct<name: string, bytes: int64, crc32: int64>>
child 0, item: struct<name: string, bytes: int64, crc32: int64>
child 0, name: string
child 1, bytes: int64
child 2, crc32: int64
duckdb: string
null_counts: struct<nipr: int64, nipr_moe: int64, nui: int64, nui_moe: int64, nic: int64, nic_moe: int64, pctui: (... 128 chars omitted)
child 0, nipr: int64
child 1, nipr_moe: int64
child 2, nui: int64
child 3, nui_moe: int64
child 4, nic: int64
child 5, nic_moe: int64
child 6, pctui: int64
child 7, pctui_moe: int64
child 8, pctic: int64
child 9, pctic_moe: int64
child 10, pctelig: int64
child 11, pctelig_moe: int64
child 12, pctliic: int64
child 13, pctliic_moe: int64
output: string
built_utc: string
sample: bool
tables: struct<estimates: int64, geographies: int64, category_codes: int64>
child 0, estimates: int64
child 1, geographies: int64
child 2, category_codes: int64
source_rows: int64
source: struct<url: string, bytes: int64, sha256: string, retrieved_utc: string, headers: struct<Content-Len (... 107 chars omitted)
child 0, url: string
child 1, bytes: int64
child 2, sha256: string
child 3, retrieved_utc: string
child 4, headers: struct<Content-Length: string, Last-Modified: string>
child 0, Content-L
...
ol
child 14, eligible_vs_within_income: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 15, all_income_denominator: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 16, complete_2024_rows: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 17, states: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 18, counties: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 19, county_view_grain: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 20, kalawao_missing: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
elapsed_seconds: double
config: struct<memory_limit: string, threads: string, max_temp_directory_size: string, preserve_insertion_or (... 12 chars omitted)
child 0, memory_limit: string
child 1, threads: string
child 2, max_temp_directory_size: string
child 3, preserve_insertion_order: string
to
{'built_utc': Value('string'), 'source': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string'), 'retrieved_utc': Value('string'), 'headers': {'Content-Length': Value('string'), 'Last-Modified': Value('string')}, 'members': List({'name': Value('string'), 'bytes': Value('int64'), 'crc32': Value('int64')})}, 'source_rows': Value('int64'), 'sample': Value('bool'), 'tables': {'estimates': Value('int64'), 'geographies': Value('int64'), 'category_codes': Value('int64')}, 'null_counts': {'nipr': Value('int64'), 'nipr_moe': Value('int64'), 'nui': Value('int64'), 'nui_moe': Value('int64'), 'nic': Value('int64'), 'nic_moe': Value('int64'), 'pctui': Value('int64'), 'pctui_moe': Value('int64'), 'pctic': Value('int64'), 'pctic_moe': Value('int64'), 'pctelig': Value('int64'), 'pctelig_moe': Value('int64'), 'pctliic': Value('int64'), 'pctliic_moe': Value('int64')}, 'checks': {'source_row_count': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'unique_keys': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'nonmissing_keys': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'geography_consistency': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'under19_categories': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'count_identity': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')},
...
tual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'percentage_range': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'nonnegative_counts': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'geography_unique': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'all_income_percentages': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'eligible_vs_within_income': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'all_income_denominator': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'complete_2024_rows': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'states': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'counties': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'county_view_grain': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'kalawao_missing': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}}, 'output': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string'), 'duckdb': Value('string'), 'config': {'memory_limit': Value('string'), 'threads': Value('string'), 'max_temp_directory_size': Value('string'), 'preserve_insertion_order': Value('string')}, 'elapsed_seconds': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
url: string
bytes: int64
sha256: string
retrieved_utc: string
headers: struct<Content-Length: string, Last-Modified: string>
child 0, Content-Length: string
child 1, Last-Modified: string
members: list<item: struct<name: string, bytes: int64, crc32: int64>>
child 0, item: struct<name: string, bytes: int64, crc32: int64>
child 0, name: string
child 1, bytes: int64
child 2, crc32: int64
duckdb: string
null_counts: struct<nipr: int64, nipr_moe: int64, nui: int64, nui_moe: int64, nic: int64, nic_moe: int64, pctui: (... 128 chars omitted)
child 0, nipr: int64
child 1, nipr_moe: int64
child 2, nui: int64
child 3, nui_moe: int64
child 4, nic: int64
child 5, nic_moe: int64
child 6, pctui: int64
child 7, pctui_moe: int64
child 8, pctic: int64
child 9, pctic_moe: int64
child 10, pctelig: int64
child 11, pctelig_moe: int64
child 12, pctliic: int64
child 13, pctliic_moe: int64
output: string
built_utc: string
sample: bool
tables: struct<estimates: int64, geographies: int64, category_codes: int64>
child 0, estimates: int64
child 1, geographies: int64
child 2, category_codes: int64
source_rows: int64
source: struct<url: string, bytes: int64, sha256: string, retrieved_utc: string, headers: struct<Content-Len (... 107 chars omitted)
child 0, url: string
child 1, bytes: int64
child 2, sha256: string
child 3, retrieved_utc: string
child 4, headers: struct<Content-Length: string, Last-Modified: string>
child 0, Content-L
...
ol
child 14, eligible_vs_within_income: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 15, all_income_denominator: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 16, complete_2024_rows: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 17, states: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 18, counties: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 19, county_view_grain: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
child 20, kalawao_missing: struct<actual: int64, expected: int64, passed: bool>
child 0, actual: int64
child 1, expected: int64
child 2, passed: bool
elapsed_seconds: double
config: struct<memory_limit: string, threads: string, max_temp_directory_size: string, preserve_insertion_or (... 12 chars omitted)
child 0, memory_limit: string
child 1, threads: string
child 2, max_temp_directory_size: string
child 3, preserve_insertion_order: string
to
{'built_utc': Value('string'), 'source': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string'), 'retrieved_utc': Value('string'), 'headers': {'Content-Length': Value('string'), 'Last-Modified': Value('string')}, 'members': List({'name': Value('string'), 'bytes': Value('int64'), 'crc32': Value('int64')})}, 'source_rows': Value('int64'), 'sample': Value('bool'), 'tables': {'estimates': Value('int64'), 'geographies': Value('int64'), 'category_codes': Value('int64')}, 'null_counts': {'nipr': Value('int64'), 'nipr_moe': Value('int64'), 'nui': Value('int64'), 'nui_moe': Value('int64'), 'nic': Value('int64'), 'nic_moe': Value('int64'), 'pctui': Value('int64'), 'pctui_moe': Value('int64'), 'pctic': Value('int64'), 'pctic_moe': Value('int64'), 'pctelig': Value('int64'), 'pctelig_moe': Value('int64'), 'pctliic': Value('int64'), 'pctliic_moe': Value('int64')}, 'checks': {'source_row_count': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'unique_keys': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'nonmissing_keys': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'geography_consistency': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'under19_categories': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'count_identity': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')},
...
tual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'percentage_range': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'nonnegative_counts': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'geography_unique': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'all_income_percentages': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'eligible_vs_within_income': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'all_income_denominator': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'complete_2024_rows': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'states': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'counties': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'county_view_grain': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}, 'kalawao_missing': {'actual': Value('int64'), 'expected': Value('int64'), 'passed': Value('bool')}}, 'output': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string'), 'duckdb': Value('string'), 'config': {'memory_limit': Value('string'), 'threads': Value('string'), 'max_temp_directory_size': Value('string'), 'preserve_insertion_order': Value('string')}, 'elapsed_seconds': Value('float64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
County and State Health Insurance Estimates
The Census Bureau's August 2026 release provides 2024 model-based health-insurance estimates for states, DC and counties. This compact DuckDB database retains published counts, percentages and 90% margins of error, with standard datapond metadata.
| Table | Description | Rows | Columns |
|---|---|---|---|
estimates |
Demographic/income estimates, overlapping groups | 402,786 | 27 |
geographies |
State and county FIPS/names | 3,195 | 6 |
category_codes |
Official category definitions | 26 | 3 |
county_uninsured selects under-65, all-race, both-sex, all-income estimates for
3,144 counties. Source missing values remain SQL NULL. The database is 14,168,064
bytes. Source ZIP SHA-256: 5ec0a0cc5cb2e4e17cf046d9f68508decfdb4d9bbb982781d94bbf56ce50347a.
-- In DuckDB with HTTPFS available:
ATTACH 'https://huggingface.co/datasets/Nason/sahie-database/resolve/main/sahie.duckdb' AS sahie (READ_ONLY);
USE sahie;
SELECT fips, county_name, pctui, pctui_moe
FROM county_uninsured WHERE statefips='36' ORDER BY fips LIMIT 3;
Python and R clients can query this file directly with DuckDB/DBI. The Python
datapond 0.1.5 and CRAN R datapond 0.1.0 clients passed HTTP range tests against
this exact artifact using a temporary registry (DuckDB 1.5.6 and 1.5.5 respectively).
Registry-based access by the sahie identifier requires the corresponding registry
entry to be merged. See build-manifest.json for identity and validation, and
the code repository for client examples.
The groups overlap, so table rows must not be added as population counts. Race detail is state-only. Match geographic vintages before FIPS joins. Kalawao County is unavailable. Zero margins of error reflect source rounding. These aggregate modeled data do not identify causal effects. See the field guide, data dictionary and the official layout.
Source: Census SAHIE, 2024, retrieved November 2, 2026. U.S. public domain as a Census Bureau work under the agency policy; no worldwide CC0 claim.
The population denominator is the Census poverty universe. pctui uses the
selected income group; pctelig uses the same demographic group at all incomes.
Published 90% uncertainty reflects model and sampling error, subject to model
assumptions, and excludes nonsampling error. Missing estimates remain NULL.
The field guide defines all measures, overlapping categories, zero-denominator
missingness, Kalawao omissions, rounding and geographic-vintage caveats.
Query the hosted database with Python or R
These direct DuckDB examples work independently of registry-PR merge status.
Install Python duckdb or R duckdb and DBI; INSTALL httpfs below installs
DuckDB's HTTP extension if it is not already present. Tested engine versions are
Python DuckDB 1.5.6 and R DuckDB 1.5.5.
import duckdb
con = duckdb.connect(config={"memory_limit": "128MiB", "threads": "1",
"max_temp_directory_size": "0B"})
try:
con.execute("INSTALL httpfs; LOAD httpfs")
con.execute("ATTACH 'https://huggingface.co/datasets/Nason/sahie-database/resolve/main/sahie.duckdb' AS sahie (READ_ONLY)")
print(con.execute("""
SELECT fips, county_name, pctui, pctui_moe
FROM sahie.county_uninsured
WHERE statefips = '36' ORDER BY fips LIMIT 3
""").fetchall())
finally:
con.close()
library(DBI)
con <- dbConnect(duckdb::duckdb())
tryCatch({
dbExecute(con, "SET memory_limit='128MiB'")
dbExecute(con, "SET threads=1")
dbExecute(con, "SET max_temp_directory_size='0B'")
dbExecute(con, "INSTALL httpfs; LOAD httpfs")
dbExecute(con, "ATTACH 'https://huggingface.co/datasets/Nason/sahie-database/resolve/main/sahie.duckdb' AS sahie (READ_ONLY)")
print(dbGetQuery(con, "
SELECT fips, county_name, pctui, pctui_moe
FROM sahie.county_uninsured
WHERE statefips='36' ORDER BY fips LIMIT 3"))
}, finally = dbDisconnect(con, shutdown=TRUE))
Expected first counties are Albany, Allegany and Bronx, with uninsured percentages
4.8, 5.4 and 8.1 and 90% margins of error 0.5, 0.7 and 0.6 percentage points.
Python returns DECIMAL percentages as Decimal; R returns numeric values. FIPS
identifiers remain strings. These are source estimates, not individual records.
Once the sahie registry entry is merged, Python datapond.connect('sahie') and
R datapond::dp_connect('sahie') provide the registered shortcut. Use Python
datapond 0.1.5 or later and released CRAN R datapond (tested 0.1.0). Before merge,
the direct examples above avoid depending on an unavailable registry identifier.
Queries run on your machine. The 14.17 MB file size is storage, not required RAM;
DuckDB's 128 MiB limit covers its buffer pool, not the entire Python/R process.
Selected columns reduce transfer, but a final LIMIT does not guarantee a small
scan. These examples use one thread and disable disk spill.
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