Fetch any HDX dataset by id (escape hatch)¶

Beyond the curated catalogue, hdx_id= fetches any of the ~21k HDX datasets by its id, with an optional resource= filter. Live public query; wrapped for offline nbval-lax.

Setup¶

Import the unified EarthLens entry point and prepare an output directory for the downloaded files.

In [ ]:

from pathlib import Path

from earthlens import EarthLens

OUT_DIR = Path('hdx_output')
OUT_DIR.mkdir(exist_ok=True)

from pathlib import Path from earthlens import EarthLens OUT_DIR = Path('hdx_output') OUT_DIR.mkdir(exist_ok=True)

Build the request¶

Configure EarthLens for the hdx backend, targeting the wfp-topline-figures dataset by its id and filtering to its *.csv resource. Construction is kept on its own statement so the download step below reads cleanly.

In [ ]:

dataset = EarthLens(
    data_source='hdx',
    variables={},
    hdx_id='wfp-topline-figures',
    resource='*.csv',
    path=str(OUT_DIR),
)

dataset = EarthLens( data_source='hdx', variables={}, hdx_id='wfp-topline-figures', resource='*.csv', path=str(OUT_DIR), )

Download the resource¶

Call download() to fetch the matching CSV into OUT_DIR. The whole call is wrapped in a try/except so the notebook degrades gracefully (skips rather than fails) when the live HDX query is unavailable offline.

In [ ]:

paths = None
try:
    paths = dataset.download(progress_bar=False)
    print('downloaded:', [Path(p).name for p in paths])
except Exception as exc:
    print(f'skipped live query: {type(exc).__name__}: {exc}')

paths = None try: paths = dataset.download(progress_bar=False) print('downloaded:', [Path(p).name for p in paths]) except Exception as exc: print(f'skipped live query: {type(exc).__name__}: {exc}')