wb-api-tools 0.2.1

World Bank Open Data helpers — Python library + CLI mirroring the Stata wbopendata surface (discovery, data, country-context, multilingual, linewrap).

wb-api-tools

Package: wb-api-tools on PyPI — repo: jpazvd/wb-api-repo.

World Bank Open Data helpers in Python (library + CLI) and Stata (wbopendata ado package). Two surfaces over the same WB API v2, with a shared YAML metadata cache so discovery commands stay fast and offline-safe.

Python package: wb-api-tools on PyPI — pip install wb-api-tools (live release version shown in the PyPI badge above). Parallel v0.x track to the upstream Stata wbopendata package (Stata Journal lineage, v18.x).

What's here

Surface	Entry point	Reference
Python library	wb_api_tools.{discovery,data,text} (re-exported at package root)	docs/PYTHON_USER_GUIDE.md
Python CLI	wb-api-tools <subcmd> (after install) or python -m wb_api_tools <subcmd>	--help on every subcommand
Stata package	src/w/wbopendata.ado (v17.4.0)	help wbopendata in Stata, or src/w/wbopendata.sthlp
YAML metadata cache	~/.cache/wbopendata/_wbopendata_{indicators,sources,topics}.yaml (XDG-aware)	populated by wb-api-tools sync

Install

From PyPI (once published — see pyproject.toml for the canonical name):

pip install wb-api-tools

From a git checkout (dev mode):

git clone https://github.com/jpazvd/wb-api-repo.git
cd wb-api-repo
pip install -e ".[test]"

Requires Python 3.11+. The Stata package is loaded by adding src/w/ and src/_/ to Stata's adopath (or installed via net install once an SSC release lands).

Quick start

The repo ships with a runnable 7-section walkthrough that exercises the whole Python surface (discovery, live describe, get_data flag matrix, enrich_country_context, wb_text formats):

PYTHONIOENCODING=utf-8 python examples/demo_pr_b_c.py

Captured transcript: docs/PYTHON_DEMO.md.

Python CLI

After pip install, use the wb-api-tools console script (or python -m wb_api_tools if PATH doesn't include scripts). Each subcommand has --help for full flag descriptions.

Subcommand	Purpose
countries	Fetch country metadata
indicators	Fetch indicator metadata (legacy CSV/parquet/yaml dump)
data	Fetch indicator data; --no-basic skips country-context auto-merge, --geo adds capital/lat/lon, --language es switches the API path
sources	List WB data sources (--all for the full set)
alltopics	List all WB topic categories
info <id>	Show full metadata for one indicator (from YAML cache)
describe <id>	Fetch fresh metadata for one indicator (live API; --language supported)
search [term]	Paginated indicator search; --source, --topic, --field, --exact
sync	Populate / refresh the YAML metadata cache from the live WB API

Example:

wb-api-tools data \
    --indicators SP.POP.TOTL,NY.GDP.MKTP.CD \
    --countries "BRA;USA;IND" \
    --date 2010:2020 \
    --geo --long --out _data/wb/pop_gdp_long.csv

Output is written to --out (.csv / .parquet / .yaml / .yml) or printed as a preview if --out is omitted.

Python library

After pip install, the package is importable directly — no sys.path hacks needed:

import wb_api_tools as wb

wb.search("poverty headcount", limit=5)
df = wb.get_data(["SP.POP.TOTL"], "BRA;USA;IND", date="2020", geo=True)
wb.wrap("long indicator title ...", width=60, fmt="stack")   # for Stata graph title()

Full reference: docs/PYTHON_USER_GUIDE.md (library + CLI + Stata-parity table).

Stata package

src/w/wbopendata.ado is the v17.4.0 dispatcher; current Phase-0-through-6 surface mirrors the Python library:

• wbopendata, sources / allsources / alltopics / info / search / describe discovery commands
• wbopendata, indicator(X) clear data fetch with noBASIC, geo, language(es), cache(days), sync
• linewrap(W) maxlength(N) linewrapformat(stack|newline|lines|smcl) for graph-title and SMCL formatting

Open src/w/wbopendata.sthlp in Stata's viewer or run help wbopendata once the package is on the adopath. The Python-side docs/PYTHON_USER_GUIDE.md §5 has a row-by-row Stata ↔ Python parity table.

YAML metadata cache

The offline metadata cache lives in a per-user XDG-aware directory (typically ~/.cache/wbopendata/ on POSIX or ~/AppData/Local/wbopendata/ on Windows; override with $WBOPENDATA_YAML_DIR):

• _wbopendata_indicators.yaml — 29,511 indicators (~18 MB)
• _wbopendata_sources.yaml — 71 sources
• _wbopendata_topics.yaml — 21 topics

Discovery commands (info, search, sources, alltopics) read from this cache for microsecond lookups. After pip install, populate it once:

wb-api-tools sync                # download + write all three YAMLs (~10 min first time)
wb-api-tools sync --commit --tag # git-commit + tag (dev mode only)

A semi-monthly GitHub Action (.github/workflows/wb_metadata_nightly.yml — file name is historical; cron runs on the 1st and 15th of every month at 02:17 UTC, so 14–17 days apart depending on month length) keeps the repo-committed cache fresh. Manually triggerable via workflow_dispatch.

Documentation

• docs/PYTHON_USER_GUIDE.md — Python library + CLI reference (Stata .sthlp equivalent)
• docs/PYTHON_DEMO.md — captured live-API transcript from the 7-section walkthrough
• docs/EXAMPLES.md — end-to-end workflows (API, Stata, Python)
• docs/AGE_BANDS.md — standard 5-year age band codes for population indicators
• examples/ — runnable Python examples
• CHANGELOG.md — per-release change log
• doc/VERSIONING_POLICY.md — semver policy + component-level .ado version headers

Development

PYTHONIOENCODING=utf-8 python -m pytest tests/   # 62 cases across discovery, wb_text, wb_api_tools

Useful Makefile targets:

make wb-update-metadata   # refresh YAML cache (v0.1.0 pipeline)
make wb-metadata          # legacy YAML builder (pre-Phase-0)
make wb-metadata-csv      # legacy CSV builder
make wb-config            # batch data pulls from config.yaml

Branch model: feature work on develop; releases tag from main. See the v0.1.0 release notes for the full PR list.

Integration

The Python CLI and library plug into:

• Makefiles / pipelines (make wb-update-metadata, cron, GitHub Actions)
• Stata workflows (export CSV → import delimited, or use the Stata package directly)
• R workflows (readr::read_csv or arrow::read_parquet)
• Jupyter notebooks for ad-hoc analysis

License

See LICENSE.md. Developed to bridge Stata wbopendata workflows with modern Python pipelines for reproducible UNICEF / World Bank style analytics.