BLS Data Library

A small, dependency-light Python library for pulling U.S. Bureau of Labor Statistics (BLS) time series — unemployment, payrolls, inflation, wages, job openings, and productivity — with pre-built series-ID helpers so you never have to hand-encode a series ID.

from bls_client import BLSClient
from bls_client.queries import employment, prices

client = BLSClient(API_KEY)

# Latest national nonfarm payrolls
client.fetch_latest(employment.nonfarm_payrolls(), years=1)

# A labeled CPI dashboard in one call
client.fetch_named(prices.cpi_dashboard(), 2024, 2025)

All 82 zero-argument query helpers are verified against the live API.

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Install

pip install -r requirements.txt        # just `requests`
# or install the package itself (editable):
pip install -e .

Configure your API key

Register for a free key (instant, no approval): https://data.bls.gov/registrationEngine/ The free v2 key allows 500 queries/day, 50 series/query, 20 years/query.

cp config.example.py config.py
export BLS_API_KEY="your-key"          # preferred — config.py reads this env var

config.py is gitignored; the env var takes precedence over anything written in the file. You can also pass the key directly: BLSClient("your-key").

Quick start

python3 examples/basic_pull.py         # payrolls, CPI dashboard, DC-region unemployment
python3 examples/custom_series.py      # building custom series IDs

Tests

pip install -e ".[dev]"                # pytest
pytest                                 # offline: 95 tests locking the series-ID encodings
pytest -m live                         # also hit the live API (needs network + BLS_API_KEY)

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What's in the box

bls_client/
├── client.py            BLSClient — batching, named fetches, catalog metadata, row flattening
├── series.py            low-level series-ID builders (LAUS, CES, CPI, PPI, OES, JOLTS, ECI, QCEW, productivity)
└── queries/
    ├── employment.py    payrolls, unemployment (LAUS), JOLTS, QCEW
    ├── prices.py        CPI, PPI, average prices, import/export prices
    ├── wages.py         OES occupational wages, ECI, ECEC
    └── productivity.py  major-sector productivity & costs

BLSClient highlights:

• fetch(ids, start, end) — auto-batches >50 series, returns {series_id: {data, catalog}}
• fetch_latest(ids, years=N) — most recent N years
• fetch_named({label: id}) — returns results keyed by your labels
• latest_obs(series) / to_rows(results) — convenience for the most recent value / CSV-ready rows

See USAGE.md for the full API and series_id_formats.md for the series-ID decode tables.

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Coverage

Survey	Helpers	Notes
LAUS — local area unemployment	state / metro / county rates, DC-region dashboard	✅
CES — payroll employment	national by supersector; state/metro	✅
CPS — household survey	national unemployment rate, participation	✅
JOLTS — job openings & turnover	openings/hires/quits/layoffs, dashboard	✅
CPI — consumer prices	all-items, core, food, energy, gasoline, …; dashboard	✅
PPI — producer prices	all commodities, final demand, food, energy	✅
OES — occupational wages	employment + wage percentiles by SOC; 15 common occupations	✅
ECI — employment cost index	total comp / wages / benefits × civilian/private/gov	✅
ECEC — employer cost levels	total compensation, total benefits	✅ (totals only)
Productivity & costs	output/hr, ULC, comp, hours × business/nonfarm/manufacturing	✅
QCEW — quarterly census	national & state private totals	⚠️ totals only via this API

Known limitations / what "complete" would add

• QCEW is only partially served by the BLS timeseries API used here (national and state-level totals work). County- and industry-level QCEW detail requires the separate QCEW Open Data API (CSV: https://data.bls.gov/cew/data/api/...). Not yet wired up.
• ECEC benefit subcomponents (health insurance, retirement & savings, etc.) need specific benefit-subcell codes from the ECEC component list; only the compensation and benefits totals are currently exposed.
• No caching / rate-limit handling. Repeated runs spend against the 500/day quota; a small on-disk cache and a friendly error on REQUEST_NOT_PROCESSED (quota hit) would help.

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Appendix: BLS API reference

Reference material for working directly with the API (the library wraps all of this).

API versions

Feature	v1 (no key)	v2 (registered)
Daily query limit	25	500
Series per query	25	50
Years of history	10	20
Net/percent changes	No	Yes
Series descriptions	No	Yes
Calculations	No	Yes

Endpoints

GET  /v2/surveys                          # all survey codes and names (see surveys.json)
GET  /v2/surveys/{abbr}                    # one survey
GET  /v2/timeseries/popular?survey={abbr}  # 25 most-requested series IDs for a survey
POST /v2/timeseries/data/                  # the time-series data endpoint

Base URL: https://api.bls.gov/publicAPI/v2

POST body (registered, all v2 features):

{
  "seriesid": ["LAUST110000000000003", "CES0000000001"],
  "startyear": "2020", "endyear": "2025",
  "registrationkey": "YOUR_KEY",
  "catalog": true, "calculations": true, "annualaverage": true
}

Period codes: monthly M01–M12 (M13 = annual avg); quarterly Q01–Q04 (Q05 = annual avg); annual A01. Status codes: REQUEST_SUCCEEDED, REQUEST_FAILED, REQUEST_NOT_PROCESSED (often = daily quota hit). Footnote codes: R revised, P preliminary, X/N unavailable.

A full real response is saved in api_response_example.json.

Bulk flat files

Base: https://download.bls.gov/pub/time.series/ — each survey folder has {prefix}.series (master list), {prefix}.data.* (observations), and {prefix}.{dimension} decode tables (area, industry, measure). Tab-delimited; handy for bulk PostgreSQL ingest. Key prefixes: la/ LAUS, ce/ CES, sm/ state-metro, en/ QCEW, oe/ OES, jt/ JOLTS, cu/ CPI-U, wp/ PPI.

Other files in this repo

• series_id_formats.md — series-ID decode tables for each survey
• qcew_field_schema.md — QCEW quarterly/annual CSV field layouts
• surveys.json — complete survey list from the API
• bls_dataset_explorer.py / .html — browsable survey catalog
• dc_md_va_unemployment.py / generate_report.py — example report generators