import duckdb
con = duckdb.connect()
con.execute("ATTACH 'https://basemap.cancerdatasci.org/data/latest/cancerbasemap.duckdb' AS cbm (READ_ONLY)")<_duckdb.DuckDBPyConnection at 0x7fe8e21a3040>Python
Everything below runs at build time against the latest release — this page’s own output is real, not copied.
Install
pip install duckdbAttach and query
Mammography screening in Colorado counties, lowest first, with the interval, the release that said so, and the county’s rurality code:
con.sql("""
SELECT u.name, o.source_release, o.value AS pct, o.lower, o.upper, o.value_status,
r.value AS rucc_code
FROM cbm.measure.observation o
JOIN cbm.geography.unit u
ON u.geo_id = o.geo_id AND u.vintage = 2026
LEFT JOIN cbm.measure.observation r
ON r.geo_id = o.geo_id AND r.source = 'RUCC'
WHERE o.source = 'PLACES' AND o.measure_id = 'PLACES:MAMMOUSE:age_adjusted'
AND o.source_release = '2025' AND u.parent_geo_id = 'state:08'
ORDER BY pct LIMIT 10
""").df()| name | source_release | pct | lower | upper | value_status | rucc_code | |
|---|---|---|---|---|---|---|---|
| 0 | Kiowa County | 2025 | 62.3 | 57.4 | 66.8 | reported | 9.0 |
| 1 | Otero County | 2025 | 63.9 | 54.4 | 72.9 | reported | 6.0 |
| 2 | Bent County | 2025 | 63.9 | 54.4 | 72.6 | reported | 9.0 |
| 3 | Baca County | 2025 | 64.2 | 54.2 | 73.1 | reported | 9.0 |
| 4 | Crowley County | 2025 | 64.6 | 54.8 | 73.7 | reported | 8.0 |
| 5 | Adams County | 2025 | 64.9 | 57.6 | 71.7 | reported | 1.0 |
| 6 | Prowers County | 2025 | 65.3 | 56.2 | 74.3 | reported | 7.0 |
| 7 | Delta County | 2025 | 65.5 | 56.7 | 73.3 | reported | 6.0 |
| 8 | Costilla County | 2025 | 65.6 | 55.8 | 74.4 | reported | 9.0 |
| 9 | Montezuma County | 2025 | 66.2 | 57.1 | 75.3 | reported | 6.0 |
Fetch with duckdb, requests or polars: Python’s default urllib user agent is blocked by the zone (HTTP 403, Cloudflare error 1010). Pin a dated folder (.../data/v2026-11-02/cancerbasemap.duckdb) for anything you will rerun.
u.vintage = 2026 is named explicitly rather than trusting o.geo_vintage — see Concepts › Time axes for why a measure’s own geo_vintage column isn’t always the vintage its geo_id actually belongs to (tracked as #21).
con.sql(...) returns a DuckDB relation; .df() materializes it as a pandas DataFrame, .arrow() as an Arrow table, or iterate it directly — whatever the rest of your analysis expects.