WFP topline figures (tabular CSV)¶

Download a small World Food Programme indicator table and read it with pandas. Live query against data.humdata.org (public, no credentials); wrapped so it stays safe under nbval-lax offline.

Setup¶

Import pandas and the unified EarthLens entry point, then prepare a local hdx_output/ directory for the downloaded file.

In [ ]:

from pathlib import Path

import pandas as pd

from earthlens import EarthLens

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

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

Download the indicator table¶

HDX is a file-writing backend, so we use download(): it writes the WFP topline CSV into OUT_DIR and returns the list of written paths. We build the request first, then call download() on its own line. The whole live query is wrapped in a try/except so the notebook stays safe offline under nbval-lax.

In [ ]:

paths = None
try:
    table = EarthLens(
        data_source='hdx',
        dataset='wfp-topline-figures',
        variables=[],
        path=str(OUT_DIR),
    )
    paths = table.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: table = EarthLens( data_source='hdx', dataset='wfp-topline-figures', variables=[], path=str(OUT_DIR), ) paths = table.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}')

Read it with pandas¶

If the download succeeded, read the first written CSV into a DataFrame and preview its shape and the leading rows.

In [ ]:

if paths:
    df = pd.read_csv(paths[0])
    print('rows, cols:', df.shape)
    df.head()

if paths: df = pd.read_csv(paths[0]) print('rows, cols:', df.shape) df.head()