parsimony-bdp
Banco de Portugal connector — Portuguese macroeconomic, monetary, financial, and external time series via the BPstat (JSON-stat) API.
Part of the parsimony-connectors monorepo. Distributed standalone on PyPI as parsimony-bdp.
Connectors
| Name | Kind | Description |
|---|---|---|
bdp_fetch |
connector | Fetch Banco de Portugal observations by domain_id + dataset_id (optional series_ids filter and date window). |
enumerate_bdp |
enumerator | Crawl the full BPstat universe (~72 K series across 212 datasets / 65 leaf domains) for catalog discovery. |
bdp_search |
connector | Semantic-search the published BdP catalog snapshot; returns ranked series codes. |
Install
pip install parsimony-bdp
Pulls in a compatible parsimony automatically. Verify discovery:
python -c "from parsimony import discover; print([p.name for p in discover.iter_providers()])"
Configuration
No configuration required — the BPstat API is open and unauthenticated.
bdp_search reads a published catalog snapshot (default hf://parsimony-dev/bdp).
Override the snapshot location with the PARSIMONY_BDP_CATALOG_URL environment
variable, or pass catalog_url= at call time.
Quick start
from parsimony_bdp import CONNECTORS
# Discover via bdp_search, then fetch. A search hit's code splits as
# domain_id:dataset_id:series_id.
hits = CONNECTORS["bdp_search"](query="economic activity coincident indicator")
code = hits.raw.iloc[0]["code"] # e.g. "48:aea9…:12099329"
domain_id, dataset_id, series_id = code.split(":")
result = CONNECTORS["bdp_fetch"](domain_id=int(domain_id), dataset_id=dataset_id, series_ids=series_id)
print(result.raw.head())
For multi-plugin composition (autoloads everything installed):
from parsimony import discover
connectors = discover.load_all()
Catalogs
BPstat has no flat "list all series" endpoint, so enumerate_bdp walks the
domain → dataset → series hierarchy. Two things make the crawl both complete
and cheap: it paginates the datasets list (domains with >10 datasets would
otherwise lose series), and it crawls each dataset at page_size=100&obs_last_n=1
(100 series per page with a one-point observation array) — about 720 lean pages
for the whole universe. Each dataset is self-checked against its declared series
count. Maintainers then run a bilingual /series/ enrichment pass (English +
Portuguese descriptions folded into the catalog description for cross-language
recall), build a Catalog snapshot (scripts/build_catalog.py), and push it to
the snapshot URL bdp_search reads — the build runs offline as a publish job,
never at query time.
Provider
- Homepage: https://www.bportugal.pt
- BPstat portal: https://bpstat.bportugal.pt
- API docs: https://bpstat.bportugal.pt/data/docs
License
See LICENSE.
Metadata
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