eolas-data 1.1.0

Python client for the eolas.fyi statistical data API (NZ, Australia, OECD)

eolas-data

Python client for the eolas.fyi statistical data API — 717+ datasets across NZ, Australia, OECD, and more, served as tidy pandas DataFrames (or polars / geopandas if you prefer).

pip install eolas-data

Quickstart

from eolas_data import Client

client = Client("your_api_key")   # or set EOLAS_API_KEY in env

# Generic
df = client.get("nz_cpi", start="2020-01-01")

# Source-specific (sets the `eolas_source` metadata)
df = client.statsnz("nz_cpi")
df = client.oecd("nz_gdp_production_annual")

# Discovery
all_datasets = client.list()
nz_only      = client.list("Stats NZ")
meta         = client.info("nz_cpi")

Get an API key at https://eolas.fyi/signup. Free plan is 10 requests/month; Starter is 100; Pro is unlimited.

Command-line interface

pip install eolas-data[cli] adds an eolas command for browsing, fetching, and scheduling — useful for shell scripts, cron jobs, and AI-agent workflows. Output auto-detects piping: rich tables in a terminal, newline-delimited JSON when stdout is piped.

# one-time setup
eolas auth set-key
eolas health

# discover
eolas datasets list --source "Stats NZ"
eolas datasets list --search cpi --json | jq '.[].name'
eolas datasets info nz_cpi
eolas datasets preview nz_cpi --limit 5

# fetch (verb matches the Python lib's client.get())
eolas get nz_cpi --format csv > cpi.csv
eolas get nz_cpi --start 2020-01-01 --format json | jq '.[].value'
eolas get sa2_2023 --format parquet --out sa2.parquet

Scheduling

Set up recurring fetches without touching crontab/Task Scheduler syntax. Works on Linux, macOS (cron), and Windows (Task Scheduler).

eolas schedule add nz_cpi --daily   --out ~/data/cpi.csv
eolas schedule add nz_gdp --weekly  --out ~/data/gdp.csv
eolas schedule add nzd_usd --cron "0 */6 * * *" --out ~/data/fx.csv   # POSIX only

eolas schedule list
eolas schedule remove nz_cpi

Daily is the default. Pre-flight check refuses to install a schedule unless your API key is configured (otherwise the job would fail silently forever).

Integrations (Enterprise plan)

Generate ready-to-run connector configs for popular data-pipeline tools — eolas becomes a one-command source for Meltano, Fivetran, or Azure Data Factory.

eolas integrate meltano             --datasets nz_cpi,nz_gdp --output ./my-pipeline/
eolas integrate fivetran            --datasets nz_cpi
eolas integrate azure-data-factory  --datasets nz_cpi,nz_gdp

The generated directory has everything needed to plug into your destination warehouse: meltano.yml, fivetran.yml, or ADF JSON resources, plus a README.md walking through the rest of the setup. Non-Enterprise users see a clear upgrade pointer; the gating lives server-side so the capability is bypass-proof.

Exit codes

Distinct exit codes per error class, for shell scripts and agents:

Code	Meaning
0	Success
1	Generic error
2	Auth (AuthenticationError, including Enterprise-gate 403)
3	Rate limit hit
4	Dataset / resource not found
5	Other API error
64	Bad usage (mirrors sysexits.h)

Geospatial

Datasets with a geometry_wkt column auto-convert to geopandas.GeoDataFrame if geopandas is installed:

pip install eolas-data[geo]

gdf = client.get("nz_addresses")                  # GeoDataFrame
df  = client.get("nz_addresses", as_geo=False)    # plain DataFrame, WKT preserved

Polars

pip install eolas-data[polars]

df = client.get("nz_cpi", engine="polars")

Plotting

Dataset is a pandas.DataFrame subclass — use matplotlib / seaborn / plotly directly. No bundled plot helper, because there's no universal "right" plot for a tidy dataset (single-series time series vs. wide multi-measure vs. WKT geometry all need different code).

import matplotlib.pyplot as plt

df = client.statsnz("nz_cpi")
df.plot(x="date", y="value")
plt.show()

Type stubs

Dataset names are exposed as a Literal so IDEs autocomplete the catalog:

from eolas_data import Client

client = Client()
client.get("nz_")    # autocomplete shows nz_cpi, nz_gdp_production_annual, ...

The list is regenerated from the live API at release time. Passing a name not in the snapshot still works at runtime — the type hint just won't autocomplete it. Catalog snapshot date is exposed as eolas_data._dataset_names.CATALOG_SNAPSHOT_DATE.

Releasing

See docs/clients.md in the eolas data repo for the tagged-release flow and PyPI token rotation.

Before each release: python -m eolas_data._regen_names to refresh the dataset name stubs from the live API, commit the change, then tag and push.

License

MIT