pyBDL 1.1.0

A Pythonic interface for Polish GUS Local Data Bank datasets.

pyBDL

pyBDL is a Python client for the Polish GUS Local Data Bank (Bank Danych Lokalnych, BDL). It offers two complementary interfaces:

• BDL access layer: convenient pandas.DataFrame results for exploratory analysis.
• BDL.api layer: low-level endpoint clients that return raw JSON-compatible dictionaries.

Installation

pip install pyBDL

Quick Start

from pybdl import BDL, BDLConfig

with BDL(BDLConfig(api_key="your-api-key")) as bdl:
    levels = bdl.levels.list_levels()
    variables = bdl.variables.search_variables(
        name="population",
        enrich=["levels", "subjects"],
    )
    data = bdl.data.get_data_by_variable(
        variable_id="3643",
        years=[2022, 2023],
        unit_level=2,
        enrich=["units", "attributes"],
    )

Configuration

BDLConfig supports direct constructor arguments and environment variables:

• BDL_API_KEY
• BDL_LANGUAGE
• BDL_FORMAT
• BDL_USE_CACHE
• BDL_CACHE_EXPIRY
• BDL_PROXY_URL
• BDL_PROXY_USERNAME
• BDL_PROXY_PASSWORD
• BDL_PAGE_SIZE
• BDL_REQUEST_RETRIES
• BDL_RETRY_BACKOFF_FACTOR
• BDL_MAX_RETRY_DELAY
• BDL_RETRY_STATUS_CODES

Explicit constructor arguments take precedence over environment variables.

API Layers

Access Layer

Use the default BDL attributes for DataFrame output:

from pybdl import BDL

bdl = BDL()
subjects = bdl.subjects.list_subjects()
data, metadata = bdl.data.get_data_by_variable_with_metadata(
    variable_id="3643",
    max_pages=1,
)

Raw API Layer

Use bdl.api when you want direct control over the underlying payloads:

from pybdl import BDL

bdl = BDL()
payload = bdl.api.data.get_data_by_variable(
    variable_id="3643",
    max_pages=1,
)
payload_with_meta = bdl.api.data.get_data_by_variable_with_metadata(
    variable_id="3643",
    max_pages=1,
)

Highlights

• Automatic pagination helpers for collection endpoints
• Sync and async endpoint clients
• Built-in rate limiting with persistent quota tracking
• Configurable retry/backoff for transient HTTP failures
• Reference-data enrichment via enrich=[...]
• Context-manager support for clean session lifecycle management

Documentation

Documentation uses MkDocs Material with mkdocstrings and mike for versioned GitHub Pages (gh-pages branch). To build locally:

uv sync --group docs
make docs
# or: uv run mkdocs serve   # live reload at http://127.0.0.1:8000

The static site is written to site/ (gitignored). On GitHub Pages (gh-pages branch, / root):

• Pushes to main update the main mike version (tip-of-tree docs).
• A successful Release workflow run deploys {semver} and moves the latest alias to that release; the site root redirects to latest.

Key documentation pages:

• Main client — BDL client, access vs API layer, context manager, async
• Access layer — DataFrame interface, enrichment, pagination, metadata
• API clients — Low-level raw-JSON clients
• Configuration — All options and environment variables
• Rate limiting — Quotas, retry, custom limits
• Examples — Jupyter notebook with real-world examples
• Changelog — Release history