Guide¶

Browse the catalog¶

page = tl.datasets.list(country="NL", limit=10)
for d in page.data:
    print(d.table, d.title)

Dataset metadata and samples¶

ds = tl.dataset("nl-domino-poi")
meta = ds.metadata()           # DatasetSummary
sample = ds.sample("geojson")  # free preview rows (csv/json/geojson/kml)

Query features in an area (spatial, paged)¶

items() addresses the collection by slug directly — the OGC collectionId is the dataset slug, so there is no metadata round-trip.

fc = tl.dataset("nl-domino-poi").items(limit=100, bbox=[4.7, 52.2, 5.1, 52.5])

Stream every feature, paging transparently:

for feature in tl.dataset("nl-domino-poi").iter_items(page_size=500):
    ...

Pull a whole dataset (bulk)¶

fc = tl.dataset("nl-domino-poi").to_geojson()        # dict (FeatureCollection)
tl.dataset("nl-domino-poi").download("dominos-nl.geojson", format="geojson")

download() streams to a temp file and atomically renames, so an interrupted transfer never leaves a truncated file at the destination.

GeoPandas¶

With the geo extra installed:

gdf = tl.dataset("nl-domino-poi").to_geodataframe()   # geopandas.GeoDataFrame

Async¶

AsyncClient mirrors the sync surface — metadata, sample, to_geojson, download, to_geodataframe, items, and iter_items are all awaitable:

async with AsyncClient(api_key="tlb_prod_...") as tl:
    ds = tl.dataset("nl-domino-poi")
    fc = await ds.items(limit=100, bbox=[4.7, 52.2, 5.1, 52.5])
    await ds.download("dominos-nl.geojson")